An investigative approach to configuring forensic electric network frequency databases

Material Information

An investigative approach to configuring forensic electric network frequency databases
Jenkins, Christopher William
Publication Date:
Physical Description:
136 leaves : illustrations, map ; 28 cm


Subjects / Keywords:
Computer crimes -- Investigation ( lcsh )
Multimedia systems -- Security measures ( lcsh )
Data encryption (Computer science) ( lcsh )
Electric network frequency
Media forensics
bibliography ( marcgt )
theses ( marcgt )
non-fiction ( marcgt )


Includes bibliographical references (leaves 129-136).
General Note:
College of Arts and Media
Statement of Responsibility:
by Christopher William Jenkins.

Record Information

Source Institution:
|University of Colorado Denver
Holding Location:
|Auraria Library
Rights Management:
All applicable rights reserved by the source institution and holding location.
Resource Identifier:
781638509 ( OCLC )
LD1193.A70 2011M J46 ( lcc )

Full Text
Christopher William Jenkins
B.S., University of Colorado Denver, 2009
A thesis submitted to the
University of Colorado Denver
in partial fulfillment
of the requirements for the degree of
Master of Science
Media Forensics

2011 by Christopher William Jenkins
All rights reserved.

This thesis for the Master of Science degree
Christopher William Jenkins
has been approved by
effM. Smith '
Jan Bialasiewicz
aIo/ 2ofl

Jenkins, Christopher William (M.S., Media Forensics)
An Investigative Approach to Configuring Forensic Electric Network Frequency
Thesis directed by Associate Professor Catalin Grigoras
This thesis is an investigative approach to the configuration of Electric
Network Frequency (ENF) databases that are built with the intent to provide an
accurate, reliable, and reproducible source of electric network frequency
variations to be used in scientific research and forensic examinations of digital
multimedia. For the ENF Criterion to reach full potential in the United States,
forensic best practices, database cross-validations, and widely accepted
methodologies should be maintained. This thesis will help guide forensic
researchers in configuring ENF databases in a manner that reinforces those goals.
Through an investigative approach, various elements of an ENF database are
explored. The introduction of this thesis starts with a broad overview of general
forensic science, then narrows the focus to defining media forensics, and finishes
with background on the ENF Criterion. Chapter two reviews the available
scientific literature on the ENF Criterion. Chapter three investigates several
elements of an ENF database and explains how they can be used to configure a
more robust, reliable, and reproducible forensic ENF database. Chapter four
proposes a system of broadcast type ENF databases where ENF is gathered in
one location and received in a remote location. Chapter five draws the
conclusions. Appendix A describes voltage regulated ENF sources, appendix B
outlines a real case application of the proposed ENF database configuration, and
appendix C explains why and how ENF works.
This abstract accurately represents the content of the candidates thesis. 1
recommend the publication of this thesis.
Catalin Grigoras

I dedicate this thesis to my sister, Jessica, who always kept my best
interests in mind, encouraged me to think things through to the end, and gave me
unfaltering support in pursuing my passions. Jessicas heart was always in the
right place and she continually went out of her way on my behalf, and for that
this dedication is the least I can do to show my gratitude. I also dedicate this
thesis to my father, William, who consistently encouraged creative thought and
taught me to never let go of my dreams. I also dedicate this thesis to my loving
mother, Stephanie, who motivated me when I was discouraged, gave me hope
when I felt helpless, and kept me focused when I would lose sight of my goals.
I could not have realized my accomplishments without the love, support, and
guidance of my family, and for that I am eternally grateful and dedicate my thesis
work to them.

I would like to acknowledge and express special thanks to my thesis
advisor, professor, and friend, Catalin Grigoras for his uncanny dedication to his
students in ensuring we were his first priority, for his unlimited patience in
helping me to understand, and his constant motivation in encouraging me to push
the boundaries, think outside the box, and find creative solutions. I would like to
extend a kind acknowledgment to Jeff Smith for his continued guidance
throughout the program and his dedication to organizing the internship. I would
also like to acknowledge Alan Cooper for his valuable participation, detailed
explanations and thoughtful insights. Alan was always quick with a response and
thorough with an answer. I would like to express thanks to Tom OBrian of NIST
for taking the time to answer my questions with germane explanations and
invaluable perspective.
It is my pleasure to extend a very special thank you to the entire team at
Target Forensic Services Lab, who provided me unbridled creative space in their
laboratories and supplied me with every resource I needed to conduct my
research during the internship.
Last but certainly not least, I am pleased to extend a most heartfelt thank
you to my friends Tony Bernal, Allysa Jordan, Tara Petty, and Jake Montenegro.
Tony always provided me a helping hand without hesitation and together with
Allysa and Tara they took care of my beloved dogs, Jericho and Beretta, while I
was out of state on the internship. Jake watched after my house while I was gone
which kept my mind at ease and he always provided thought provoking insights
into my research. I cannot express enough gratitude to my friends for ensuring
that my dogs, my house, and the life I put on hold would be there waiting for me
when I returned from my thesis semester/intemship program.
I could not have completed this thesis without each and every one of the
people mentioned here and for that I am grateful beyond words.

1. Introduction.............................................................13
1.1 Introduction to Forensic Science........................................13
1.2 Introduction to Media Forensics.........................................21
1.3 Introduction to Electric Network Frequency..............................28
1.3.1 AC Electricity.........................................................29
1.3.2 Digital Recorders.....................................................31
2. Review of the Literature.................................................33
2.1 Summaries of Scientific literature......................................33
2.2 Coinciding Theories about the ENF Criterion.............................43
2.3 Conflicting Theories about the ENF Criterion............................44
2.4 Existing ENF Databases..................................................45
2.5 Further Directions for the ENF Criterion................................48
3. Investigating the Forensic ENF Database Configuration for use in
Digital Media Authentication.........................................50
3.1 The NCMF ENF probe......................................................51

3.2 Atomic-radio clock/source clock synchronization
3.2.1 NIST Radio Synchronization...........................................59
3.2.2 NIST Internet Synchronization.......................................60
3.2.3 NIST Global Positioning System Synchronization......................61
3.3 Sampling frequency..................................................63
3.3.1 Advantages of High Resolution ENF Databases..........................65
3.3.2 Resolution/Fast Fourier Transform Settings..........................65
3.4 Sound card............................................................66
3.4.1 Input Fevel..........................................................67
3.4.2 Signal to Noise Ratio (SNR).........................................68
3.5 Type of storage (HDD vs. SSD).........................................69
3.6 Direct Current (DC) Bias and Frequency Bias...........................74
3.7 Distortions...........................................................75
3.8 Network failure/Uninterrupted Power Supply (UPS) and safe guards......85
3.9 Advances in ENF database configuration................................86
3.10 Other areas to pay attention to......................................91
3.10.1 Proposed Changes to ENF Thresholds.................................92
3.10.2 Neutral Interference at the Signal Source..........................92
3.10.3 ENF Database Manager...............................................93

4. Proposal for Broadcast-Type Forensic ENF Databases.................94
4.1 Scope of Broadcast-Type ENF Databases.............................94
4.2 Frequency-Modulation Databases....................................95
4.3 Bluetooth Databases...............................................98
4.4 Wi-Fi Databases.................................................100
5. Conclusions.......................................................103
Appendix A...........................................................109
Appendix B...........................................................110
Appendix C...........................................................123

1 United States Electrical Grids.................................31
2 Probe Output Waveform.........................................52
3 Proposed Schematic for ENF Probe.............................52
4NCMF ENF Probe..................................................54
5 ENF Probe LPF..................................................55
6 NIST GPS Common-View Satellite Communications................58
7 NIST GPS Time Accuracy Over 24-Hours (09/10/2011)............63
8 Continuous Waveform to Discrete Waveform......................65
9 Evidence Signal to Noise False Alarm Probability.............69
10 ENF Database HDD Write-Error (7,200 RPM)......................70
11 ENF Evidence Deletions........................................71
12 Comparisons of Three ENF Files................................73
13 Differences in two files from the same A/D output.............73
14 60 Hz Signal Sampled at 8 kHz.................................76
15 60 HZ Sampled at 110 HZ.......................................77
16 Sony PCM-D50 Sampled at 22 kHz................................78
17 RecAll Pro Sampled at 22 kHz..................................79
18 RecAll Pro Sampled at 8 kHz...................................80
19 Distorted Signal..............................................82
20 Peak to Peak Jitter Measurements.............................83

21 RecAll Pro Timer Settings.......................................87
22 RecAll Pro Audio File Settings.................................87
23 ENF Database Acquisition System................................90
24 Suggested ENF Database Structure...............................90
25 Three ENF Extraction Methods...................................91
26 ENF Database Manager...........................................93
27 FM Radio Broadcast.............................................96
A1 Regulated Power Supply.........................................109
B1 Event Log (A)..................................................Ill
B2 Event Log (B)..................................................112
B3 Task Schedule..................................................113
B4 Task Action....................................................114
B5 Signal to Noise Ratio (SNR)....................................115
B6 2011 -10-23 01:00 Write Error..................................117
B7 DC bias on MN-ENF PCI..........................................119
B8 44.1 kHz Sine Wave Sweep.......................................120
B9 MN-ENF-PC1 Aliasing............................................121
Cl North American Balancing Authorities...........................125

1 Tested ENF Probe Component Values...............................84
Cl 1,000 MW Affect on Frequency...................................128
C2 MW Required to Change ENF by 0.1 Hz............................128

1. Introduction
As a disclaimer, I do not endorse any of the products or software
presented in this thesis. I do not have any financial interest or connections with
the products or software presented in this thesis. I am in no way affiliated with
the companies that produce the products or software presented in this thesis. The
products or software presented in these slides are only mentioned as tools for
forensic analysis and the intention of this thesis is solely educational.
This thesis is about Electric Network Frequency (ENF) databases and
how they can be configured to provide accurate, reliable, and reproducible results
for use in scientific research and forensic examinations of digital media. Before
jumping straight to the complex interworking of the ENF Criterion and what a
forensic database means, a general overview of forensic science is presented.
Then the focus is narrowed to describe media forensics, and then further focused
to audio authentication, and finally an introduction to the ENF criterion is
presented to help the reader understand how ENF came about and why a forensic
database is necessary. The reader will gain a broad understanding of forensics
and how audio authentication is used in the field.
1.1 Introduction to Forensic Science
The Merriam-Webster dictionary defines forensic as relating to or
dealing with the application of scientific knowledge to legal problems". The
same dictionary defines science as knowledge or a system of knowledge
covering general truths or the operation of general laws especially as obtained
and tested through scientific method [36], Logically it would follow that
forensics is the argumentative science of applying a system of knowledge to
solve legal problems. Forensics is a combination of several scientific fields to aid
in the discovery of facts for answering legal disputes. From anthropology to
zoology, there is a forensic application for almost every scientific discipline
today but the history of applying science to answer legal questions has a history
almost as old as jurisprudence itself. Some sources place the first applications of
forensics as far back as 700BC when the Chinese used thumbprints in clay
sculptures and on documents to preserve the identity of ones possessions [37], In
250BC Greek physician, Erasistratus, noticed that the heart rate of his patients
would rise when they were lying [38], Roughly around 1235AD Chinese author
Song Ci published the first literature on applying medical science to solve crimes
involving death [39], One of the most recognized early applications of forensic
science in a legal dispute leading to a conviction comes from France where in

1840AD Mathieu Orfila found traces of arsenic in the body of Charles Lafarge
after conducting the Marsh exam, this evidence led to the conviction of Lafarges
wife for murdering her husband [40],
Throughout history, forensics has helped people understand how events
took place by applying scientific reason to the scene of a crime as well as helping
judges and juries to understand where the accountability of events should lie by
making identification, comparison, and authentication possible through the
science of DNA, fingerprints, handwriting, ballistics, and the list can be
extended. Forensics has aided legal disputes for centuries in several types of legal
systems from Roman law to Napoleonic Code to Religious law and eventually
spawning out into what the Western world knows today as the two basic concepts
of law, adversarial and inquisitorial. The adversarial system descends from
common law and the inquisitorial system descends from civil law. Forensics will
continue to aid the trier of fact in any legal system well beyond our lifetimes and
even in legal systems not yet imagined because applying scientific knowledge to
legal problems is the most logical way to discover the truth of the matter.
The adversarial system of law governs the United States, which allows
for both sides of a dispute a fair chance to pose their argument in front of a judge
and jury. In most circumstances the jury is the trier of fact ultimately making the
decision about the outcome of the dispute. In some instances, such as most traffic
violations, there is no jury and the plaintiff makes their case without
representation to the judge alone. In more complex disputes such as murder
trials, civil hearings, and child pornography cases; attorneys commonly utilize a
wide range of outside perspective on the case in the form of expert testimony to
elaborate their point. In the adversarial system experts enter the court after going
through a deposition or Daubert hearing or some hybrid of a Daubert/Frye voir
dire hearing depending on the state [41], At which point the expert is allowed to
testify as to their opinion of the matter based on the scientific conclusions they
have deduced from analyzing the evidence in the case. Many forensic analysts
find themselves on the witness stand at some point in their careers, testifying
about the work that they have conducted and offering their opinions in an
unbiased manner to the jury.
The experts opinion will ultimately play a partial role in the jurys
decision about the case. The jury is faced with the task of weighing the opinions
from all the experts, other witness testimony, and evidence of the case into their
ultimate decision. In a Daubert hearing the judge holds the role of gatekeeper
making the decision about whether or not to allow a witness to testify in front of

the jury as an expert. In addition to measuring the relevancy of an expertise,
during a Daubert hearing, the trial judge measures the reliability of an experts
testimony against this non-exclusive checklist: the expert must be able to
demonstrate the theory or technique is falsifiable, refutable, and testable; the
basis of their opinion has been subjected to peer review and publication; the
techniques used in arriving at such conclusions have a known or potential error
rate; there are existing methods and maintenance of standards and controls
concerning the operation of those methods or techniques; or the theory and
technique is generally accepted by a relevant scientific community [42],
Providing proof of a majority of these five Daubert standards is relatively easy
and a forensic examiner can be qualified as an expert with a minimal amount of
training on the subject, this however, does not mean that a minimal amount of
training should be acceptable or that the Daubert standards should be taken
The Frye standard on the other hand, states that the basis for the experts
opinion must be sufficiently established to have gained acceptance in the
particular scientific field [43], The United States Supreme Courts opinion on
this matter came from an expert attempting to use the systolic blood pressure
deception test as a basis for his deduction. The court held that the testimony was
inadmissible and the defendant was found guilty of second degree murder. Some
states in the US still uphold the Frye standard but the majority of US states have
adopted the Daubert standard.
There is no clear-cut generalized standard for the level of education a
forensic examiner must have, thus some forensic disciplines require more
training and certification than others. Latent print analysis for example, is a
position that an examiner holding a certificate from a one-semester community
college course and a one-year internship can find themselves sitting. Forensic
psychology on the other hand, is a field where having an advanced degree such
as a PhD, EdD, or PsyD, will be required to make it past the Daubert/Frye/voir
dire. Recently more attention has been paid to the field of forensics and the
judicial impact of an experts testimony in general; the industry is starting to
trend toward educational requirements across the board. In 2009 a
congressionally mandated report was generated by the National Academy of
Sciences (NAS Report) declaring that there are serious deficiencies in the
nations forensic science system [44]. The report elaborated by making a call for
major reforms concerning new research, mandatory certification programs, and
stricter protocols for analyzing and reporting on evidence, as well as the gradual
shift of forensics out of law enforcement and into the private sector. Any changes

that come as a result of the NAS report will take several years to come to fruition
but forensic agencies should be aware of the implication in the NAS report.
Forensics can have a positive impact on the outcome of a case by providing
concrete interpretations of the evidence leading to the conviction of a perpetrator
or the release of an innocent suspect. On the other hand, forensics can have
catastrophic impacts on the judicial system by providing wrong, biased, or
misinterpreted results leading to the conviction of an innocent suspect or the
release of guilty perpetrator. Forensics is a discipline with a high magnitude of
responsibility, a discipline that should require a minimum education at the
masters level, as suggested in the NAS report. To further illustrate this point a
few landmark cases will be presented where forensic science failed to provide
solid scientific findings and had irreversible impacts on completely innocent
Case 1: Scotland, United Kingdom, 1997 [45], Murder case of Marion
Ross. A fingerprint was found near the victims body that led detectives to
believe that Shirley McKie, Scotland Police Officer, had entered a restricted area
of the crime scene. This event sparked a chain-reaction that would eventually
lead the well-respected officer to a life of fear and isolation. David Grieve, US
fingerprint expert who was called upon to cross examine the findings in the
Marion Ross murder case, once commented on finger print analysis by stating
Errors of this magnitude within a discipline singularly admired and
respected for its touted absolute certainty as an identification process
have produced chilling and mind-numbing realities. Thirty-four
participants, an incredible 22% of those involved, substituted presumed
but false certainty for truth. By any measure, this represents a profile of
practice that is unacceptable and thus demands positive action by the
entire community" [46].
Shirley McKie, victim of judicial error by wrongful forensic science in
the Marion Ross case, was acquitted after Mr. Grieve gave his testimony.
Eventually McKie was compensated £750,000 but not until after her lively-hood,
career, and personal life had been completely devastated by embarrassment,
cover-up, and false accusations. To this day McKie lives a quiet life working in a
friends shop and avoiding police at any cost due to the phobia she has developed
from the trauma that nearly drove her to divorce, abandonment, and suicide.

Case 2: Madrid, Spain, March 11, 2004 [47], A series of explosions
targeting the mass-transit train system brought Europe to a screeching halt. Less
than a month later on May 6th the United States Federal Bureau of Investigation
(FBI) arrested an Oregon attorney, claiming that a fingerprint found on a
backpack near the bombings belonged to Brandon Mayfield. Mayfield was held
for more than two weeks as a material witness in undisclosed locations with no
contact to legal counsel or his family. Even after Spanish authorities found the
finger prints to belong to someone other than Mayfield, the FBI persisted in their
prosecution of Mayfield. Eventually, when Spanish authorities released their
position on the matter, international news spread to the US and the FBI released
Mayfield. A series of lawsuits ensued from Mayfields counsel in the Supreme
Court, eventually compensating Mayfield 2 million dollars. Luckily for Mayfield
his wife and family stood by him throughout this ordeal. Unfortunately, many
wrongfully convicted people leave prison to find that their spouses have
remarried, their children have left, and their family has abandoned them, leaving
them with nothing except an extremely difficult to expunge criminal record that
blocks them from decent jobs and homes.
Case 3: Dallas County, Texas, 1985 [48], Finger print analysis is not the
only forensic science that has led to judicial errors. Media forensics has been
misused in several cases and has led to the wrongful convictions of completely
innocent people. On February 4, 1986 David Pope was brought to trial for the
aggravated sexual assault of a Texas woman. A man had broken into the
womans apartment, held a knife to her throat, and raped her. After the attack, the
rapist called her several times over the phone and she worked with police to
record the phone calls. The woman wrongfully identified Pope in a line up
claiming that his hair color was similar to her attackers. Larry Howe Williams, a
Houston Police Officer, conducted a voice-print analysis and comparison of the
phone recordings made by the woman and voice samples taken from Pope.
Williams had completed a 2-week voice print identification course in Somerville,
New Jersey on voice-print analysis and was a member of the International
Association of Identification which was enough to satisfy the requirements to
enter the court as an expert on the subject. Williams was wrong in his analysis
and contributed faulty science to a case that led the jury to find Pope guilty. After
serving 15 years in prison for a crime he had nothing to do with, Pope was
exonerated on February 2, 2001 becoming the first person exonerated through
DNA testing in Dallas County.
Case 4: Romania, March 21, 2011 [49]. An expert, George Pop, working
for the Romanian Ministry of Justice submitted his forensic report to the

Romanian Supreme Court of Justice in criminal case number 10753/1/2008.
EdiTracker was used in the experts analysis of the authenticity of a digital audio
recording. In the EdiTracker program, a Russian software based on non-validated
methods, digital audio shall be native 44.1kHz. The evidence recording was up-
sampled from 8kHz. The expert claimed that up-sampling the signal did not
introduce any distortions to the evidence. However, in figure 5 of the experts
report a graph shows the spectrum of the up-sampled signal with nearly 15dB FS
(Full Scale) amplification between 4kHz and 6kHz. The up-sampling jeopardized
the integrity of the evidence when this process introduced these kinds of signals
between 4 kHz and 6 kHz, and more than that, the up-sampling from 8 kHz to 44
kHz added new samples to the signal, new frequency components, and masked
potential previous traces of manipulation. The distortions introduced by up-
sampling the signal were evident, it is not clear if the expert did it because he
didn't know how to deal with basic signal processing or if he counterfeited the
recordings for the prosecution, the Court refused to provide the defendant a clone
of the evidence recordings, and no further investigation has been open against the
Case 5: Romania, June, 30 2011 [50], Another forensic report from the
Romanian Ministry of Justice was submitted in the Court of Appeal Brasov
criminal case number 6367/2/2010. In this report Pop was trying to answer to the
argument that opposing counsel had risen about the necessity to up-sample
digital audio so that it can be used in EdiTracker. The expert sent an email to the
STC developers of EdiTracker, asking them to please clarify the issue. In the
email response, the STC EdiTracker developers stated that: should have
the file sampling rate over 44.1 kHz.... But when the expert translated this email
from English to Romanian for his Expertiza Criminalistica he stated that: .. .you
should have the file sampling rate lower than 44.1 kHz.... This is a case of
expert unfairness and will ultimately have unfortunate judicial implications.
Case 6: Boise, Idaho, April 21, 2011 [52], Dennis Walsh, a former New
York City detective and self-proclaimed audio examiner was rejected as an
expert by US District Judge Lynn Winmill in the Edgar Steele Conspiracy case.
Steele was accused of conspiring to hire a hit-man to murder his wife and
mother-in-law after a pipe bomb was found attached to the underside of his
wifes car and the hired hit-man confessed to the FBI. An audio recording of a
conversation between Steele and the hit-man had its authenticity brought into
question and Walsh conducted an analysis of the audio for the defense. Walsh
used EdiTracker in his analysis and the Judge ruled that: /just have to conclude
he does not have the background and experience after noting that the

qualifications on Walshs CV had been inflated and that key-points in the report
duplicated the other proposed experts report verbatim, and that Walsh does not
hold any education or certification as a forensic examiner. Dave Snyder, certified
forensic examiner and electronics engineer for the FBI stated that: ...the
uncertified, Russian-made testing program (EdiTracker) that Walsh used
detected several anomalies but the program is unreliable and thats why the
FBI doesn 7 use it".
Case 7: Romania, November 16, 2009 [51]. Truica-Zevedeanu Alin-
Nicolae is sentenced to prison after a photography/video expert, Alexandra Vase,
from the Romanian Intelligence Service (Serviciul Roman de Informatii in
Romanian language) submitted a facial comparison between ATM video footage
and suspect photos made of Alin-Nicolae. The comparison between the images
was sub-par at best, having not been lined up correctly, placed on the same plane,
or compared in a scientific manner. A forensic IT expertise provided no digital
evidence against Alin-Nicolae and no scientific evidence to support the
hypothesis that he had anything to do with the original crime, but he fell victim
to faulty media forensic science, and thus he remained in prison for over seven
months until a former Romanian Internal Affairs and Ministry of Justice image
expert presented an accurate comparison of the evidence. After the new report
was submitted Alin-Nicolae was immediately released from prison.
Case 8: Bucharest, Romania November 11, 2011 [73] [74], Recent
developments in Romania have uncovered a baffling affair in the Eastern
European country. In Romania the inquisitorial system of law is used and there
are no private or independent forensic experts in Romania, as there are here in
the US and many other places around the world, including most of Europe.
Because there are no private experts, this means that all forensic examinations
are carried out by state forensic experts. When a panel of judges requests a
forensic examination they turn to the Romanian Ministry of Justice National
Institute of Criminal Expertise (INEC) which oversees the interactions between
forensic experts and judges. There have been several outcries that the recently
appointed director of INEC, a state prosecutor Catalin Ceort, was placed in his
position in debatable circumstances. The same Romanian expert mentioned in
cases 4 and 5, Gheorghe Pop, entered the Romanian court in early November
2011 and claimed that he was required to use un-validated methods in his
forensic analysis because of a protocol between the National Anticorruption
Department (Departamentul National Anticoruptie in Romanian language) and
INEC, where Pop is employed as an examiner. On November 11, 2011 Ceort
released a statement that there is no such protocol between the two agencies. The

entire affair around these kinds of state expertise without any scientific
background, non-credible experts, contradictory statements in the Court under
oath, and further official statements by the Ministry of Justice, seems to develop
like any corruption affair, affecting many branches of the state system including
the Romanian public education system. Politehnica University of Bucharest
created a Masters of science program in Media Forensics in Romania, launched
in the fall of 2011, with faculty members that have no experience in forensic
media, they have no published papers in peer-reviewed journals, no scientific
research, and no expertise in the field. It is obvious that these kinds of programs
look more like a business then a scientific program, and the effects are
predictable: a non-credible faculty will generate a non-credible Masters
program, and non-credible students, which will further infest the Romanian
judicial system claiming that they have a M.S. diploma and that they can be
media forensic experts, like any other respectable scientist from abroad that did
scientific research, published peer-review articles, and got experience in Court
cases. The connections between the Romanian Ministry of Justice, Politehnica
University of Bucharest and INEC will generate results that are produced for the
highest bidder and not produced for forensic science, completely disrespecting
forensic science. Unless Romania opens the doors to outside experts, like the
European Union has been requesting since 2006, then real forensic science may
never enter a Romanian court room again. For more information about this
scandal please see references [73] and [74], the web pages are in Romanian but
can be translated with a search engine.
Special attention shall probably be paid by the so called strategic
partners of Romania who, in turn, support these practices; they speak positively
about certain currently empowered politicians and judicial reforms, while the
facts don't support their political or diplomatic statements. It is hard to believe
that a country that promotes these kinds of practices in forensic expertise and
their judicial system can be a strong, credible, and respectable partner in an
international coalition. Any vulnerabilities or weakness of one of the members
can irreversibly affect the credibility of the entire alliance.
There are standards throughout all forensic disciplines to help keep
judicial errors from occurring, these standards are not always respected however,
through neglect, bias, or ignorance. Evidence is required to have a chain of
custody that clearly details who had possession of the evidence and when they
had it for example. Another example is that evidence should never be changed,
like writing files to an evidence hard drive. The forensic examiner has no control
over the circumstances in which the evidence was created, nor does the examiner

have any control over the chain of custody up until the evidence is delivered for
examination. The forensic examiner does however have control over the way the
evidence is analyzed; this is where using scientific methodologies is crucial. The
examiner has control over how the analysis is conducted and interpreted; this is
where bias can be introduced into the examination and the examiner should strive
to mitigate bias. The examiner has control over the way the opinion is reported;
this is where the examiner can become an advocate for the counsel instead of an
advocate for the science, an examiner should never advocate for the counsel.
Professional associations and societies have rules set forth to manage behavior
such as ethics violations and biased opinions among the members. Disregarding
such rules can end an experts scientific career.
Throughout the history of forensic science there have been disciplines
that still flourish today and others have not stood the test of time. Phrenology for
instance, was the pseudoscience of determining particularities about a persons
intelligence, demeanor, and personality by making measurements of the skull.
For nearly 100 years phrenology stood up to scientific standards and there was
even a phrenology society based out of Edinburgh, Scotland. By the mid 1800s
the American Phrenological Journal was being published in New York [53].
Eventually the science of phrenology was debunked and is viewed today as a
source for judicial error and substantially groundless assumptions without any
scientific foundation. There are examples of other forensic fields that get more
credit than they deserve; bite mark analysis, ear print analysis, and hand writing
analysis are a few examples of forensic disciplines that have not had any great
advances since the time of their creation and require a very minimal amount of
training to become a court accepted expert. Even though they require a minimal
amount of training these forensic sciences still play an important role in the
courtroom today. Across the nation evidence is entered into the court every day
that requires the attention of a forensic expert including bite-mark, ear print, or
handwriting evidence and everyday new crimes are committed that require the
attention of new techniques and strict methods which is even more reason to
uphold the NAS report. As Edmond Locard stated, Every contact leaves a trace
[54]. However, in the dawn of the digital age, technology allows criminals to
commit crimes that require no contact and commit crimes that leave no trace.
1.2 Introduction to Media Forensics
In order for a fingerprint expert to conduct their analysis a finger must
make contact with an object; for a handwriting expert, a pen must make contact
with paper; for a ballistics expert, a bullet must make contact with the rifling of a

barrel; for a DNA expert, biological traces must be left at the crime scene. In
media forensics on the other hand, there is no direct contact between the speaker
and the microphone; there is no contact between the camera and the object being
photographed; there is no contact between the files being downloaded and the
computer user; there is no contact between a text message and the person who
sent them. In media forensics there is the absence of contacts and traces because
advances in technology have created a technical barrier between the perpetrator
and the crime scene; simultaneously creating the need for advances in the way
forensics is applied to such digital crime scenes. There are several aspects to
media forensics, the main concept being that media forensics is applying digital
science and a system of digital knowledge to answer legal questions concerning
digital evidence. Digital knowledge is comprised of the science of digital audio
recordings, digital image recordings or stills, digital files from computers, cell
phones, GPS systems, and the list can be extended. After thousands of years of
forensics having the advantage over criminals through scientific methods,
forensics is now faced with the challenge of catching up with the advances in
technology that criminals have taken advantage of. Just as murderers could use
arsenic on their victims undetected until the chemist, James Marsh, developed the
Marsh exam modem criminals will continue to use technology in ways that
defy the examiner until the scientific truth is exposed through advances in
scientific forensic media research. One recent development in media forensics
helps to authenticate audio recordings. The demand to authenticate audio
recordings was pushed to new limits during the 1970s when perpetrators took
advantage of the lack of knowledge surrounding what constitutes an original and
continuous recording.
On June 20, 1972 in the Executive Office Building of the President of the
United States, an audio recording was created on analog tape using hidden
microphones [55], This tape, among others, became the center of one of the
largest scandals in American history, leading the nation to question the ethics and
integrity of the government as a whole; as well as leading to the resignation of
President Richard M. Nixon. This moment in history is referred to as the
Watergate Scandal and has been the source of many books, movies, and pop
culture parodies. Media forensics did not exist in 1972 but through the work of
six audio experts, brought together during the Watergate investigation to identify
the source of an 18-minute gap on the tape from June 20, 1972, the foundation
for audio authentication was created.
Today, the standards of audio authentication closely resemble the
methodologies, techniques, and analyses used by the Watergate audio

authentication panel to determine the authenticity of the tape. The six person
panel consisted of four PhDs from MIT and two MS Electrical Engineers, all the
members had backgrounds in electrical engineering and had contributed
significant research in the fields of acoustics, psycho-acoustics, under-water
acoustics, digital signal processing, speech analysis, tape recorder development
and manufacturing, and more. Whether those six panelists knew it or not they
followed the same principles that other forensic sciences had been following for
centuries; do not change the evidence in anyway, reduce bias in the analysis and
conclusions, and cross verify the findings. Looking back from todays
perspective with the strict guidelines on examining audio evidence, the panel
respected all of the standards that would be expected of them today, even though
those standards did not exist in 1972.
The methodologies that the panel used are still widely accepted and used
to this day; there have been several advances in the field of audio authenticity
since 1972 but given what they had to work with, they utilized very strong
methods and techniques in their analysis. The panel developed a logical approach
to determine the authenticity of the Watergate tape; they followed a seven step
method that consisted of critical listening, magnetic marks, waveforms, spectra
of speech and buzz, phase continuity and speed Constance, flutter spectra, and
other tests and measurements including searching for splices, measuring azimuth
alignment, and measuring for bias signals. They also investigated the claimed
original recording equipment and made test recordings. Today, when analog tape
analysis is needed, these procedures are still used as a guideline; there have been
advances in analog tape analysis since 1972 such as magneto-optical tape
development which makes the magnetic markings on the tape visible. Critical
listening, waveform, spectrum, and phase continuity analyses still uphold to
modem challenges when dealing with digital audio. Since the early 1970s there
have been advances in audio authentication methods because the audio
technology has morphed from analog to digital. Even though this thesis focuses
on the latest method in digital audio authentication, a brief description will be
provided to catch the reader up to speed on what audio authentication methods
look like today in a digital world and why the distinction between sound and
audio is important as a closer look is taken into media forensics.
Sound and audio surround people at almost every moment of every day,
from traffic noise to background sounds and cell phone conversations to MP3
music and entertainment. This also means that sound and audio are fast becoming
key elements in many crimes and consequently key elements in many civil and
criminal cases. All evidence brought into the courtroom is subject to scrutiny

from the opposing side and seemingly simple definitions can be twisted to
convince a jury that solid evidence is null. For this reason, it is necessary to
distinguish the difference between audio and sound. Sound is the human
perception of an acoustic wave as it propagates through a medium such as air,
water, or steel. Pressure waves generated from a sound source such as a loud-
speaker, propagate through these mediums and excite the neighboring particles
until the pressure wave hits the human ear. Once in the ear canal the signal hits
the ear drum which moves three small bones on the backside of the eardrum
called the hammer, anvil and stirrup known collectively as ossicles [56], The
ossicles transfer sound pressure from the outer ear to the inner ear like a system
of levers which activate the fluid filled cochlea. The cochlea is full of tiny hair-
like follicles that are connected to nerves and this mechanical motion is
transduced into a signal which the brain interprets as sound.
Audio on the other hand, is an electrical signal which travels through
wires and circuits such as a signal being sent through a digital recorder, for
example, once the audio-electrical signal reaches the loud-speaker on a cell
phone it is then transduced from an audio signal into an acoustic signal which the
brain interprets as sound. There is also scientific reasoning for this distinction
between audio and sound. With audio and sound the wavelengths are calculated
using the same formula where wavelength is equal to the speed of the signal
divided by the frequency ( = c/f) [57]. The difference is that sound signals
traveling through a medium have a much lower speed than audio signals
traveling through circuits. The speed of a sound signal propagating through air is
roughly 1,130 feet per second at sea level and 70 degrees Fahrenheit, where an
audio signal travels roughly at the speed of light or 982,080,000 feet per second
in a vacuum. Clearly the wavelengths for sound and audio are going to have
different measurements. Sound is transduced into audio in several ways, be it by
talking into a cell phone, recording an instrument, or capturing a conversation
with a recorder, but the principle is always the same: Sound propagates through a
medium until it reaches a microphone, then the acoustic pressure waves are
transduced into an electrical value and then sent to a microphone preamplifier to
boost the signal level and then to an analog to digital (A/D) converter (see
section 3.3) and finally to a digital signal processing unit or other storage media.
In forensics, when dealing with questioned recordings audio is the term
used. A forensic examiner cannot make precise calculations or explain error rates
based on what they hear, due to the fact that human perception of sound is
subjective. Precise measurements and calculations can only be made on audio
which has finite values which will be the same no matter who is conducting the

examination (unless its analog audio stored on a magnetic media, which will
naturally deteriorate to some degree every time the media is played back). Many
times the authenticity of a questioned audio recording will be brought up during
trial and there are several tools available to the forensic examiner to verify
whether a recording is authentic.
Recent advances in recording technology have created a simple and
sometimes undetectable process for the alteration of digital audio recordings.
Forensic examiners have several tools at their disposal for authenticating digital
audio recordings such as physical inspection, metadata analysis, critical listening,
high-resolution waveform analysis, narrow-band spectrum analysis,
spectrographic analysis, phase continuity, statistical analysis, and digital data
analysis [18]. Because of the rapid developments in recording technology a
complete list of authentication tools will not be covered in this thesis, the most
common and widely accepted digital audio authentication tools will be discussed
here. Each forensic case is unique and new audio formats will require new
techniques. One recent and emerging technique has proven to be a powerful
authentication tool in Europe and will continue to foster new growth and
scientific research in the US. Before discussing this new technique, a brief
summary of the authentication tools listed above will be presented.
According to the Audio Engineering Society an authentic audio recording
is one made simultaneously with the acoustic events it purports to have
recorded, in a manner fully and completely consistent with the methods of
recording claimed by the party who produced the recording, and free from
unexplained artifacts, alterations, additions, deletions, or edits [58], The
Scientific Working Group on Digital Evidence (SWGDE) defines an authentic
audio recording as the first manifestation of sound in a recoverable stored
format be it magnetic tape, digital device, voicemail file stored on a server,
optical disk, or some other form" [59]. Over the years there have been several
techniques for authenticating audio recordings that have developed into a widely
accepted protocol and are briefly described below.
Physical inspection is an examination of the evidence to determine if
there are physical indications of alterations or tampering to the evidence. Some
physical indications of tampering could be found in the form of tape splices for
Metadata analysis is the examination of the information that the computer
reads in binary, hexadecimal, or ASCII form. The examiner can utilize

specialized software to view this information and extract valuable information
such as creation date/time, recorder make/model/serial number, length of the
recording, and signs of audio editing software used on the recording among other
Critical listening is the aural examination of the evidence recording using
high quality headphones on a professional playback system. Critical listening can
expose areas in the recording that should be examined more closely. Some
common events to listen for are pops, clicks, and sudden changes in background
noise, interrupted speech, and discontinuities in foreground noise. Critical
listening cannot substitute statistical observations but can pin-point areas that
should be examined more closely.
High-resolution waveform analysis is a visual examination of the
relationship between the time and amplitude characteristics of the recording on a
graphical display where time is on the X axis and amplitude is on the Y axis.
Most any audio editing software can display waveforms allowing the user to
zoom in on areas of interest, even to the sample level. This analysis can reveal
suspect areas where the values between two consecutive samples show a drastic
Narrow-band spectrum analysis is also a visual examination where the
frequency content is represented on the X axis and the amplitude content is
represented on the Y axis. By dissecting the audio into small windows and
calculating the average for each window this analysis can reveal what the
amplitude is for each frequency band in the evidence recording. This is useful for
quickly determining factors such as the amplitude of a 60 Hz signal, digital
aliasing, and sample frequency bandwidth among other factors. By decreasing
the size of the FFT window from 512 to 4096, for example, the examiner can
increase frequency resolution while decreasing time resolution. Conversely,
increasing the size of the FFT window can increase time resolution while
decreasing frequency resolution.
Spectrographic analysis is another visual examination where the audio
signal is represented on a graph displaying time on the X axis and frequency on
the Y axis, areas of high amplitude are usually light in color and areas of low
amplitude are usually dark in color. This type of analysis can quickly reveal
constant tones, speech formants, and certain types of edits that interfere with a
broadband of frequencies among other information.

Phase continuity is the comparison between discrete tones captured in the
evidence recording with reference tones generated from software. This type of
analysis works better with discrete tones that span the length of the recording,
have a substantial signal to noise ratio, and fall within the high frequency
Direct Current bias or DC bias can be caused by poor quality analog
components, a problem with the A/D conversion process, or other sources. DC
bias can be detected if the averaged values of a time vs. voltage waveform equal
something other than zero. Sometimes DC bias can be used to help determine if
an evidence recording is consistent with the recording device claimed to have
created it.
Frequency bias can be caused by a recording device clock functioning
improperly. When a recording device clock is sampling the signal too fast or too
slow then frequencies will be shifted lower or higher. Frequency bias can be
detected if the nominal frequency is subtracted from the time vs. frequency
waveform and the result is something other than zero. Frequency bias can also be
helpful in determining particularities of a certain recording device.
Statistical analysis involves extracting information about the numeric
relationships in the recordings digital structure. This type of analysis can reveal
useful information about the consistency of repeated bytes and can also be used
to compare test recordings to the evidence recording.
Digital data analysis is a technique used to find information about the bits
and bytes in the metadata of the recording. Digital data analysis is a lot like the
statistical analysis but digital data analysis is carried out on an image file of the
recording and not the actual recording file itself.
Because digital technology evolves so rapidly, and different techniques
used in authentication also develop rapidly, the techniques listed above should
suffice in giving the reader a general overview of the most common methods and
help to explain what forensic examiners are looking for when conducting an
analysis. To learn more about digital audio authentication the Bruce Koenig and
Doug Lacey AES paper [18] offers an in depth exploration of the techniques
listed above and more.
Another important note on audio authentication is that the claimed
original recording device should be submitted with the evidence recording. If the

forensic examiner has the claimed original recording device there are several
more techniques that can be used to establish authenticity. An example for analog
recordings is that recording events such as stop, start, pause, and record over
leave unique frequency signatures on analog tape. The physical distance between
these unique signatures can be compared with the physical distance between the
record, play back, and erase heads in the claimed tape recorder. Digital audio
devices can be examined to ensure that the format of the evidence recording is
consistent with the normal operations of the recording device. In both analog and
digital authentication the claimed original recording devices should be examined
and test media should be produced from them to produce as much information as
it is possible about the authenticity of the evidence. With digital media it is
possible for a forensic examiner to determine that the evidence is not authentic.
But for a forensic examiner to find that the evidence is completely authentic is an
impossibility; due to the limitations in digital media that face the forensic
examiner the closest opinion that the examiner can reach to completely authentic
is that the evidence is consistent with an authentic recording.
1.3 Introduction to Electric Network Frequency
The latest development in digital audio authentication comes from Dr.
Catalin Grigoras, National Center for Media Forensics (NCMF), University of
Colorado Denver. Grigoras discovered that small variations occurred over time in
the frequency of electrical power. These small variations led to a huge discovery
that eventually developed into what is known today as the Electric Network
Frequency (ENF) Criterion. ENF analysis consists of extracting the power line
hum from a digital audio recording and comparing it to a database to determine
date and time of creation, areas of potential additions or deletions of audio, and
the geo-location where the evidence was created.
To understand ENF one must understand two basic concepts: First,
Alternating Current (AC); second, digital audio recorders. These two concepts
combined make possible the authentication of digital audio by comparing the
power-line hum embedded in the recording to a database of grid activity at that
point in time. In a basic overview, AC electricity is produced and consumed
continuously on a power grid. The differences in production and consumption
create small variations in the frequency of the grid. These small variations are
nearly the same at any two points on the grid at any given moment, fluctuating
constantly but in unison. When a digital audio recorder is powered by the grid
(plugged into an outlet) or in the proximity of an electromagnetic field (power
lines, refrigerator, ect.) the recorder not only records the intended speech, music,

or other sound of interest but the recorder may also record the small variations
coming from the grid frequency.
Later, when the recording becomes evidence and is given to a media
forensic expert for examination, the expert can extract the recorded variations of
the power-line signal from the recording and compare this signal to a reference
database that will indicate exact date, time, geo-location, potential additions,
potential deletions, and mixed material in the recording. To further elaborate the
concept and origins of ENF, the two fundamental concepts of AC electricity and
digital recorders will be presented below.
1.3.1 AC Electricity
AC electricity can be produced in a variety of ways, the most popular
methods being hydroelectric, nuclear reactors, and coal power. Hydroelectric
methods typically use a water wheel and the gravitational force of falling water
to get a turbine spinning. Coal and nuclear energy produces steam and use steam-
pressure to get a turbine spinning. Whatever method is used to get the turbines
spinning the concept is the same after that point. AC electricity is produced by a
spinning turbine connected to a generator that rotates at 60 cycles per second in
the US and at 50 cycles per second on the UCTE grid. The speed of the rotation
of the generators creates the transmission frequency that the voltage is
transmitted from the power plants to the end user. The electricity from the power
plants goes to a transmission substation where a transformer boosts the voltage to
hundreds of thousands of volts in order to prepare it for its long journey to the
end user. High voltages such as 500,000 volts help reduce line-loss while the
power is being transmitted. Due to differences in produced and consumed
electricity, the generators may rotate at slightly different speeds than exactly 60
cycles per second. There are strict thresholds that maintain rotation speeds within
roughly 0.2 Hz on the US Eastern grid for example. Because the generators rotate
in tandem with each other, the rotations per second are generally nearly identical
throughout a given grid. A generator rotating as much as 2 Hz out of sync with
the others can quickly generate enough heat to destroy itself.
When the power comes off the transmission grid it goes to a power
substation where it is stepped down from the high transmission voltages to
something on the order of 10,000 volts. At this point the power can be distributed
in different branches to the end user. Electricity generated from power plants in
the continental United States provide electricity to the Western Grid, Eastern
Grid, and the Texas Grid [22], [60], [65], The Eastern and Western grids also

extend north into Canada [12] but Quebec is on an independent grid. This
electricity is transmitted from the power substations to the end user in the form of
alternating current at sixty cycles per second 60 Hz US (50 Hz UCTE) just as it
was at the power plant. Differences in the amount of electricity being produced
and the amount of electricity being consumed cause the frequency of the grid to
vary over time. This balance leaves a distinct signal that is always fluctuating,
always random, highly non-predictable, and has a high-probability of never
repeating roughly between 59.5 Hz and 60.5 Hz on the US grids and roughly
between 49.5 Hz and 50.5 Hz on European and United Kingdom grids [22],
These small variations between established thresholds can be captured with an
ENF probe plugged into a mains power source such as a wall socket. The ENF
probes output is a line-level signal that can be plugged into a computer
soundcard or other audio interface and recorded. Simultaneous ENF recordings
from the same grid will be nearly identical because each grid carries a consistent
phase across the grids entire span. For the United States, this was tested and
confirmed by Professor Rich Sanders [12] at the National Center for Media
Forensics (NCMF) as well as the physical extension of the Eastern and Western
grids into Canada.
Theoretically, only four ENF databases are needed to establish 24-hour a
day monitoring of ENF activity throughout the continental United States and
Canada. The geographic regions that are covered by the Western grid ENF
database include everything generally west of the Rocky Mountains extending
from the southern borders of the United States and north through Canada. The
geographic areas covered by the Eastern grid ENF database include everything
generally east of the Rocky Mountains extending from the southern borders of
the United States and north through Canada, minus Texas and Quebec. The
geographic area of Texas has a separate electrical grid and will require its own
ENF database. The geographic area of Quebec also has a separate electrical grid
and will require its own ENF database. Four ENF collection sites established in
strategic locations can provide useful information about digital audio recordings
made within a region of roughly 7.6 million square miles. Likewise, any digital
recording device that is introducing ENF into the recorded digital audio will have
the nearly the same ENF trace as the rest of the electrical grid it was created on at
that moment in time, which is a unique trace in time due to the differences in
production and consumption. For this reason digitally recorded audio can be
compared to an ENF database to establish a date/time stamp, geo-location,
potential edits, deletions, additions, and mixed material. Figure 1 displays the
three electric grids of the continental United States. See Appendix C for more

Figure 1 United States Electrical Grids
1.3.2 Digital Recorders
Digital audio recorders are devices that turn continuous waveforms into a
discrete stream of Is and Os. There are several types of digital recorders available
on the market from rack-mount recorders to hand-held units. Even video cameras
with the ability to record audio fall into this category. The idea is the same in
almost every type of digital recorder. A pressure wave propagating through a
medium is picked up by the microphone and then amplified; or a line-level signal
is received, then either the microphone signal or the line-level signal is
transmitted to the low-pass anti-aliasing filter to meet the requirements of the
Nyquist Frequency, then passed on to the sampler or analog to digital A/D
converter, which samples the analog signal at fs (Sampling Frequency) values per
second and assigns a binary word to represent amplitude (bit depth). This process
results in a digital representation of the original sound or signal with fs values for
the time axis and X bit values for the amplitude axis.
This data stream is then saved to a storage format such as a Hard Disk
Drive (HDD), Solid State Drive (SSD), or flash memory. This data stream can be
reproduced any number of times and is more resilient to degradation than analog
media. Because of this ability to reproduce binary words any number of times,
making the distinction between original, first, or second generation copies

becomes nearly impossible. HASH values can even be calculated to verify that
the copied material is an exact representation of the original and at that point
there is almost no way to distinguish the two. Digital audio can also be edited in
ways that analog audio could never be edited, defying the forensic examination.
The microphones used for picking up sound can also pick up
electromagnetic variations through the air and embed this information into the
recorded audio; this is because the microphones, leads, and recorder circuitry can
act like capacitors and inductors picking up the presence of electromagnetic
fields, similar to how certain metal objects can act as antennas when connected to
a power source. Even if a digital recorder is powered by batteries alone and not
mains power, ENF traces can sometimes still be extracted simply because the
recorder was in the presence of electromagnetic fields coming from a variety of
sources such as refrigerators, computers, or power lines. Whether the recording
device is powered by the mains or powered by batteries, if ENF is embedded into
the recording then a comparison can usually be made against an ENF database.
When building an ENF database for forensic purposes, ensuring that the
recorded signal satisfies standards for forensic analysis is crucial. The ENF
signal shall be free of clipping, lossy compression, and distortions, the signal to
noise ratio (SNR) shall be as high as possible, and the acquisition system clock
shall be synchronized with an atomic-radio clock or grid independent time
controller. Using an ENF database to compare reference and questioned ENF
involves precise measurements of amplitude, spectrum, and zero-crossings in
order to accurately time-stamp, discover potential edits, and authenticate digital
audio/video recordings. There are several precautions that should be respected in
order to maintain the integrity of a forensic ENF database. In this thesis
suggestions will be made regarding fifteen aspects of the database configuration
that should be viewed as helpful guides in order to build and maintain a robust,
secure, reproducible, reliable, and accurate forensic ENF database that will
satisfy forensic standards and best practices.

2. Review of the Literature
This chapter reviews the available scientific papers that have contributed
to the ENF criterion. These papers have been published from across the globe
and collectively offer a broad range of perspective on the ENF criterion. Each
paper is given a short summery and then a discussion about the coinciding
theories and conflicting theories is presented. Next, explanations about further
directions that are potentially available for the ENF criterion are discussed. Then
this chapter concludes with an overview of existing ENF databases.
2.1 Summaries of Scientific Literature
[1] Digital Audio Recording Analysis: The Electric Network Frequency
(ENF) Criterion
by Catalin Grigoras
This article was published by the journal of Speech, Language, and the
Law in 2005 and introduced the Electric Network Frequency criterion to the
forensic community as a valuable tool for the authentication of forensic digital
audio recordings. Written by Dr. Catalin Grigoras during his tenure at the
National Institute of Forensic Expertise in Bucharest, Romania, this article
outlines his research and begins with an introduction to the process of
authenticating digital audio. Grigoras then explains the fundamentals of the ENF
criterion on the European 50 Hz grid and stresses the importance of compiling a
database to be used in comparing evidence recordings against a reference.
Graphs displaying his analysis of the ENF help guide the reader through his
findings. Grigoras initiated three experiments to verify his theory by monitoring
the variations around 50 Hz in different locations in his office building,
elsewhere in the city of Bucharest, and three different geographic locations
across the European grid. The results show that for all consumers, at any
moment in time, the ENF values are the same. This discovery laid the
groundwork for an entirely new tool for authenticating forensic digital audio.
Grigoras explains in a real case example how the ENF criterion was used
to determine where deletions had been made in a disputed digital audio recording
between two speakers. By comparing the evidence recording to the reference
database, Grigoras was able to verify the claims made by speaker A that several
deletions had been made, as well as, determine the length of time that had been
obfuscated by each deletion, and conclude the exact date, hour, minute, and
second that the recording had been produced. Grigoras explains the

spectrographic method he used to extract the ENF from the evidence recording
and he shows the 2D-spectrograms used to visually identify alterations in the
recording. Explanations of the software used in this case are presented with the
settings of the down-sampling, band-pass filtering, and FFT size functions used
to extract the ENF.
In the following section, Grigoras presents another method of ENF
extraction, known as the Zero-Crossings Method (ZCR). This method is a time-
domain analysis of the ENF as opposed to the frequency-domain analysis used in
the Spectrographic and Long Term Average Spectrum (LTAS) methods. By
analyzing the ENF in the time-domain Grigoras was able to compute the zero-
crossings of the signal and measure the time differences of consecutive zero-
crossings then he used these values to plot the signals traces on a graph with
enough resolution to see the micro variations in the ENF. The ZCR method can
reveal enough detail about the ENF to see differences in quantification levels if
there are audio segments coming from different recording equipment and spliced
In his article, Grigoras explains his database configuration that has been
continuously monitoring ENF activity on the European 50 Hz grid since May
2000. He also explains the storage requirements for such a configuration,
followed by a flow chart for automatically detecting the date and time of a
questioned recording. The limitation of extracting ENF from analog recordings is
briefly outlined to stress that the ENF criterion is designed for digital audio
recordings where the original ENF signal must not be distorted during extraction.
This is a very well written paper and it lays the foundation for using the Electric
Network Frequency as a tool in forensic authentication of digital audio.
[2] Application of the Electric Network Frequency (ENF) Criterion a Case
of a Digital recording
by Mateusz Kajstura, Agata Trawinska, Jacek Hebenstreit
This article was published through the Forensic Science International 155
(2005). The authors conducted their contributing research for this article at the
Institute of Forensic Research in Cracow, Poland. The aim of this article was to
verify the ENF criterion and the reliability of the extraction methods. The authors
employed a series of tests involving different types of recorders located in
different areas around Poland recording simultaneously. Later the recordings
were processed and compared to verify that equipment in different places of the
same grid will capture the same grid fluctuations at that moment in time. The

authors also acquired some information from the power company, which samples
the ENF once every second, and made a comparison of the power companys
values against the values that the authors were seeing in their tests. This
confirmed that the ENF criterion could be used to establish the authenticity of a
digital audio recording in Poland. The authors also tested and verified the ability
to detect edits, particularly deletions, using the ENF criterion.
A real case example is given in this article where the authors were asked
to authenticate a 55 minute recording between two business men. The date
claimed by the business men was different then the date in the metadata of the
recorder by about 196 days. Using the ENF criterion, the authors were able to
verify that the recording was consistent with the ENF fluctuations on the date
that witnesses of the conversation claimed. It was later determined that the
person operating the recorder neglected to appropriately set the time and date of
the recording device. The authors conclude that the ENF criterion is a valid
forensic tool for authenticating questioned digital audio recordings in Poland.
Poland, like most European countries, is part of the Union for the Coordination
of Transmission of Energy (UCTE).
[3] Dating of Digital Audio Recordings by Matching of Electrical Network
Frequency Patterns
by Francisco Javier Simon del Monte, Jos Bouten, Catalin Grigoras, Joaquin
This is a technical presentation from 2006 given to the European
Academy of Forensic Sciences in Helsinki, Finland. This presentation explains
the complex mathematics behind the long term average spectrum, zero-crossing
extraction methods and automatic pattern matching methods employed to search
databases automatically. There are several helpful charts and equations in this
|4] Applications of ENF criterion in Forensic Audio, Video, Computer and
Telecommunication Analysis
by Catalin Grigoras
This article was published by the Forensic Science International in June
2006. This article investigates the application of the ENF criterion to determine
the integrity of forensic audio/video, computer, and telecommunication systems.
The article starts with an in depth explanation of the ENF criterion and the
extraction methods. An early version of a forensic ENF database is presented

here as well as an outline of several experiments carried out by the author to
establish the validity of the ENF criterion over the European electrical network.
Three case work examples are discussed involving: 1) a digital audio recording
between two speakers of a three hour length, 2) a video with audio, and 3) a
video with audio that was broadcast on television. In all three cases the author
successfully employed the ENF criterion to determine the authenticity of the
audio/video recordings establishing dates, times, and mixed material. Using the
ENF criterion it was the authors opinion in case three that three ENF signals
could be found. ENF1 coming from the video camera, ENF2 coming from the
broadcasting company, and ENF3 coming from the questioned tape containing
the video transmitted recording. This article further solidifies the validity of the
ENF criterion and expands the applications of the criterion to video with audio,
computer, and telecommunications.
[5] An Investigation into the Electrical Network Frequency (ENF)
Technique for Forensic authentication of Audio Files
by Nisha Morjaria
This is the thesis work of Nisha Morjaria and constitutes an investigation
into the applicability of the ENF criterion on analog tapes. The author explains
the experiments that were undertaken to arrive at the conclusions. The author
verified that simultaneous recordings in three areas around the United Kingdom
captured the same ENF variations at that moment in time. The experiments show
that visual correlations could be seen between the three recordings that were
made to analog tape at three separate geographic locations in the United
Kingdom. Conducting more complex examinations such as zero-crossing and
automated database search are highly unlikely to occur with analog evidence due
to the inherent wow and flutter from the mechanical motions inside the tape
machines. This article is the first step in verifying that the ENF criterion can be
employed on the United Kingdom electric grid.
[6] Further Investigation into the ENF Criterion for Forensic Authentication
by Eddy B. Brixen
This article was presented at the 123 Audio Engineering Society (AES)
conference in 2007. The author investigates ways to establish reference data,
spectral contents of the electromagnetic fields, the effect that low bit rate codecs
have on low frequency hum, and tracing ENF harmonic components. This article
suggests that multiple databases should be connected to the same grid to help
protect from losing data in the event of localized power outages. The author

approaches the above investigations in a statistical manner and provides several
helpful graphs to help elaborate the point. The author also stresses the point that
there are standards and best practices for authenticating analog audio but that
there needs to be the same protocols for digital audio. This article further verifies
that the ENF criterion can be extended to apply to Denmark, which shares the
same UCTE grid as Romania.
[7] Techniques for the Authentication of Digital Audio recordings
by Eddy B. Brixen
This article was presented at the 122nd AES convention in 2007. This
article further explains the need for a solid methodology that can span
international boarders when authenticating digital audio. This article starts with a
brief introduction to challenges facing experts when they present digital audio
analysis into court and then explains several other digital audio authentication
tools. The author also discusses digital recorders and explains how the
microphones can pick up ENF signatures from being in the proximity of
electromagnetic fields with no physical connection to the grid. In closing the
author mentions ways to detect edits and ways in which the ENF criterion can be
[8] Applications of ENF Analysis Method in Forensic Authentication of
Digital Audio and Video Recordings
by Catalin Grigoras
This article was presented at the 123rd AES conference in 2007. This
article reports on different ENF types, phenomenon that determine ENF
variations, analysis methods, stability over geographic areas in Europe, internal
laboratory validation, uncertainty measurements, real case examples, effects of
different compression algorithms, and potential problems that the forensic
examiner can encounter when employing the ENF criterion. The author also
discusses quality control measures for establishing a forensic ENF database.
[9] Frequency Disturbance Recorder Design and Developments
by Lei Wang, Jon Burgett, Jian Zuo, Chun Chun Xu, Bruce J. Billian, Richard
W. Conners, Yilu Liu
This article was published in 2007 by the Institute of Electrical and
Electronics Engineers (IEEE). This article discusses the complex network that
monitors electric grid disturbances across the continental United States and parts

of Canada called the Frequency Monitoring Network (FNET). FNET is
comprised of several Frequency Disturbance Recorders (FDR) strategically
placed around the United States and Canada. These recorders generate statistical
data that is used to monitor and control the frequency variation on the electrical
grid interconnects. Virginia Tech has been involved with this project for several
years and has been instrumental in establishing this network. FNET is a database
of ENF variations; FNET is not a high resolution database of ENF variations
however. FNET can supply statistical data that is averaged in 1 minute windows
or 10 minute windows, this information can be useful for monitoring overall
network performance but for a forensic application of the ENF signal a high
resolution database is necessary to be able to detect the differences down to the
second and to employ automated searches. The techniques and methods in this
article imply that the ENF criterion should be as valid in the United States as it is
in Europe.
110] ENF; Quantification of the Magnetic Field
by Eddy B. Brixen
This article was published by AES at the 33rd International Conference in
2008. This article takes a statistical approach to quantifying the magnetic field
and the effects it can have on recording equipment. The author conducted several
experiments to quantity this phenomenon including the use of calibrated
magnetic field strength detector, a device made to generate magnetic fields, and
several types of digital recorders. This article has several helpful charts and
graphs that help give a visual aid to explain the experiments and the results.
|11] The Electric Network Frequency (ENF) as an Aid to authenticating
forensic digital audio recordings an Automated Approach
by Alan J. Cooper
This article was published in 2008 at the AES 33rd International
Conference. The author employs a statistical approach to establishing an ENF
database of nominal values instead of audio recordings. The advantage to this
type of database configuration is that storage requirements are small, automated
database searches are made easy, and the database is a relatively simple but
powerful and an accurate means to acquire ENF. The author employs a Short
Time Fourier Transform that calculates the peak magnitude in short windows,
then the windows are overlapped and calculated again and again for the length of
the recording. This results in a very accurate representation of the ENF signal

suitable for automatic processes. This article establishes the validity of
employing the ENF criterion on the United Kingdom electrical grid.
[12] Digital Audio Authenticity Using the Electric Network Frequency
by Richard W. Sanders
This article was written by the founder of the National Center for Media
Forensics in 2008, published at the AES 33rd international conference. This
article explains the first nationwide ENF experiment in the United States. The
results explained in this article solidified the ENF criterion validity and potential
for application in the United States. The author also proposes a schematic for an
ENF probe based on a 3-resistor voltage divisor. The experiment involved
synchronized recordings of ENF fluctuations in different locations on the
Western Grid, synchronized recordings of ENF fluctuations in different locations
on the Eastern Grid including the physical extension of this grid into parts of
Canada, and synchronized recordings of ENF fluctuations in different locations
on the Texas Grid. The results verify that the ENF criterion has potential for use
on the United States interconnects and the article contains several helpful charts
and graphs to help illustrate this point.
[13] An Automated Approach to the Electric Network Frequency (ENF)
Criterion: Theory and Practice
by Alan J. Cooper
This article was published by the International Journal of Speech
Language and the Law in 2009. In this article the author expands on his previous
paper and further explains the automated approach used to configure the ENF
database at the Metropolitan Police Forensic Laboratory in the United Kingdom.
The author explains how the ENF time-domain signal is split into overlapping
frames and how each frame has the peak value calculated and how this is
averaged over time to create the ENF signature. This is a very logical and robust
approach to establishing an ENF database.
[14] Forensic Speech and Audio Analysis Working Group (FSAAWG) Best
Practice Guidelines for ENF Analysis in Forensic Authentication of Digital
by Catalin Grigoras, Alan J. Cooper, Marcin Michafek
This is a forensic best practice guideline that was published by FSAAWG
in 2009. This best practice guideline walks through the step-by-step procedures

for extracting ENF and gives some suggestions as to how the forensic ENF
database should be configured.
[15] Using the ENF Criterion for Determining the Time of Recording of
Short Digital Audio Recordings
by Maarten Huijbregtse, Zeno Geradts
This is a paper written by a student at the Netherlands Forensic Institute.
This paper explains that using maximum correlation coefficient is a more robust
method to match ENF patterns because frequency offsets will affect the Mean
Square Error and zero-crossing methods. In addition, the author proposes that the
correlation coefficient (CC) is a more robust approach over Mean Quadratic
Difference (MQD). In essence the author empirically shows how CC is more
accurate when applying automatic search algorithms to digital audio evidence
that has word-clock errors from the recorders sampling clock.
[16] Applications of ENF Analysis in Forensic Authentication of Digital
Audio and Video Recordings
by Catalin Grigoras
This article was published by the AES in September 2009. This article
expands on the authors 2007 AES article. This article covers the majority of the
content presented in his former article with the addition of a section on ENF
influences which covers the phenomenon of digital recorders capturing the ENF
trace while in the presence of electro-magnetic fields. The format of the article is
basically the same but additional information is presented in almost every
[17] The Application of Power-line Hum in Digital Recording Authenticity
by Marcin Michafek
This article was published by the Institute of Forensic Research (Krakow,
Poland) in 2009. This article explores the algorithms for automated ENF
database searches against evidence digital audio recordings. The author also
speaks of the tests that were undertaken in writing this article to see the results of
an automated discontinuity check. This article is a purely statistical approach to
automated searching and follows much of the same logic presented in Coopers
previous articles. There are several graphs and charts that help illustrate the

[18] Audio Authenticity: Detecting ENF Discontinuity With High-Precision
Phase Analysis
by Daniel Patricio Nicolalde Rodriguez, Jose Antonio Apolinario, Luiz Wagner
Pereira Biscainho
This article was published by IEEE in September 2010 and offers a very
interesting approach to the ENF criterion. This article proposes a method for
detecting phase discontinuities in an ENF signal which can help reveal areas of
edits that have been added or deleted. The main idea is that even without a
database, the continuity of the ENF signal can be determined to be either
continuous or altered by examining the phase of the captured evidence ENF
signal. In the introduction, a brief overview of the ENF method is explained and
then that ties into section two which gives the fundamental origins of the ENF
signal. Section three covers how complex algorithms can estimate the phase of a
sinusoidal signal. Section four covers the method for employing this phase
estimation during audio authenticity, including a visual approach and an
automatic approach. Section five evaluates these methods using real examples.
Section six covers practical issues such as investigating the practicality of this
method when applied to a 60 Hz network from Rio de Janeiro, where a slight
increase in error rate was encountered. Section seven concludes that the idea of
finding abrupt phase changes in the power grid signal is a favorable method for
finding edits in audio recordings when the ENF database is unavailable. The
main lesson to take from this article is that the authors propose a fourth type of
ENF sub-database that samples the ENF at 12 kHz.
[19] Building a Database of Electric Network Frequency Variations for use
in Digital Media Authenticity
by Jeff M. Smith
This paper was written by Smith, who worked closely with Rich Sanders,
while Interim Director of the NCMF, and presented at the 2010 scientific
meeting of the American Academy of Forensic Sciences. It explains the ENF
database that was originally installed at the NCMF as well as a brief explanation
of the ENF database that Grigoras maintains in Romania and the ENF database
that Cooper maintains in the United Kingdom. The author explains how there
needs to be certain standards in place in order for the ENF criterion to reach its
full potential in the United States. The author also briefly explains the FNET
system described in the Wang et al IEEE article.

[20] Advances in ENF database configuration for Forensic Authentication of
Digital Media
by Catalin Grigoras, Jeff M. Smith, Christopher W. Jenkins
This article was published by AES at the 131st convention in 2011. This
article explains the advances in forensic ENF database configuration and is an
instrumental step in establishing forensic best practices that can transcend
international borders. This article gives a brief background on the ENF criterion
and then explains the methods used at the National Center for Media Forensics to
develop the modem ENF probe. The authors then explain how a secure,
redundant, and reliable ENF database should be configured.
[21] Forensic Authentication of Digital Audio Recordings
by Bruce Koenig and Douglas Lacey
This article was published by the AES in 2009 and is the closest
manuscript there is to a standard methodology in the field of digital audio
forensics. This article walks the reader through several digital audio
authentication techniques in a methodical manner that can almost be followed
like a check list. Not every single method for authenticating digital audio can be
covered since the advances in digital audio are developing every day. This article
does give a solid foundation to the science of authenticating digital audio.
[22] Statistical Tools for Multimedia Forensics
by Catalin Grigoras
This article was published by the AES at the 39th international
Conference in 2010. This article extends the list of digital audio authentication
tools to include compression level analysis. When a digital recording is edited the
user must save the file with the edits, which introduces a second layer of
compression. The levels of compression can be detected and used to verify if a
recording has undergone multiple generations of compression. There are several
graphs to illustrate the point and this article takes a purely statistical approach to
analyzing digital audio evidence.

2.2 Coinciding Theories about the ENF Criterion
The ENF criterion has been tested in Romania, the United Kingdom,
Denmark, the Netherlands, Poland, Denver, Canada, at various other points
across Europe as well as all three grids in the United States and has consistently
shown three important concepts. First, fluctuations in an electric grid leave
unique signatures over time. Second, these fluctuations are the same when
measured simultaneously from any two points on the same electric grid. Third,
digital audio recording equipment can capture these variations and the recordings
can be analyzed to determine date, time, hour, minute, second, potential
additions/deletions, mixed material, and broad geographic location.
The methods for extracting ENF are widely accepted [1-23], The
spectrographic analysis is the simplest approach and usually is enough to satisfy
the needs of the matter. The spectrographic method is a visual comparison of an
evidence recording to the ENF database. Both the database and the evidence are
down-sampled to twice the nominal frequency of interest plus 20%, and then
both files are band-pass filtered with a width of about 1 Hz around the nominal
frequency of interest. Utilizing a high FFT order it is possible to see small
variations around the nominal value very closely and determine similarities and
differences, areas of missing or added material, or multiple ENF traces.
If more complex analysis is required the next step is the Fast Fourier
Transform (FFT) approach which samples the audio in short windows, computes
the maximum magnitude frequency in each window based on the power
spectrum of that window. Then the windows overlap each other by a determined
amount, usually one sample offsets, and then the process is repeated. In this
method it is also necessary to down-sample and band-pass filter the signal
accordingly. Cooper developed a similar method using Short Time Fourier
Transforms (STFT) on the 100th harmonic of the ENF signal and zero-padding
the time values to alleviate the time versus frequency tradeoffs inherent with
FFT. These methods are computational approaches that can produce statistical
and robust results.
Another computational approach is the time-domain zero-crossings
method. This method calculates consecutive zero-crossings and the length of the
semi-period and builds a comparative graph from those values.

2.3 Conflicting Theories about the ENF Criterion
From the available literature on the ENF Criterion, one conflicting theory
was found. The conflicting theory does not concern the phenomenon that makes
ENF work nor does it concern the way that ENF databases are configured, but it
is a crucial point that needs to be clarified because if automatic search algorithms
are implemented incorrectly during a forensic examination then judicial errors
may soon follow. The conflicting theory has to do with the way in which
automatic search algorithms are applied to the ENF Criterion. Automatic search
algorithms are a powerful tool that can add a statistical quantification, known
error rates, and unbiased results to the forensic analysis; these are important
attributes for using the ENF Criterion in US courts. In addition, automatic search
algorithms save the examiner from the daunting task of visually comparing
unknown evidence ENF to reference ENF. Simon del Monte et al. [3], Cooper
[11], and Huijbregtse et al [15] presented both Mean Square Difference (MSD)
and Correlation Coefficient (CC) algorithms. The goal of both algorithms is to
automatically find a date and time in an ENF database that is most consistent
with unknown evidence ENF recordings. Even though both algorithms are
inherently different mathematically, the appropriate steps for both methods
involves pre-processing such as down-sampling, band-pass filtering, and
ensuring frequency bias sample rate offsets are accounted for. Once the pre-
processing has been correctly and uniformly conducted across all the
examination material (evidence and reference) an accurate comparison can be
made between different automatic search algorithms.
Huijbregtse et al states that 7/ is seen that the ENF criterion failed in
correctly estimating the time of recording for 44 out of the 70 recordings" [15].
Huijbregtse et al demonstrated that using the MSD algorithm on ENF recordings
that had been pre-processed inappropriately without mean subtraction, caused the
automatic search method to return 44 out of 70 wrong results. On the other hand,
Huijbregtse et al demonstrated that computing the CC with mean subtraction for
automatic database search resulted in only 3 out of 70 wrong matches. In
essence, Huijbregtse et al compared one method without first subtracting the
mean values (correcting frequency bias) with a method that had the mean values
subtracted. The reason that this type of mistake can be easily overlooked is that
CC naturally resolves any issues pertaining to frequency bias sample rate offsets
that are caused by the word clock in the evidence recording device performing
poorly. In other words, the CC used in Huijbregtse et al experiment subtracts the
mean values as part of the algorithms product, which is how frequency bias

sample rate offsets are corrected. MSD on the other hand, requires an additional
step before applying the algorithm to correct frequency bias.
This conflict of opinions is a good example of how the scientific
community can learn about the scientific process in question. Cooper explains
these differences clearly and makes a convincing case for MSD in his 2011
International Journal of Speech, Language, and the Law article [23], Cooper
explains how the CC works under the hood as well as the MSD. Cooper then
conducts an experiment on 50 mock-evidence recordings each 22.5 seconds in
length (not too short to return all wrong matches and not too long to return all
correct matches). What Cooper proves is that MSD returned 13 out of 50 correct
matches and CC returned only 4 out of 50 correct matches. The 4 matches
identified by the CC were also identified by the MSD. In addition, Cooper
calculated the next best 30 matches and found that the MSD had 23 lowest order
correct best matches where CC had zero. By using statistical analysis, Cooper
demonstrates the superiority of MSD over CC when the pre-processing has been
correctly and uniformly conducted across all the examination material. Coopers
findings [23] are fully supported by the NCMF tests conducted during 2010
2011, by the students coordinated by Grigoras and Smith, showing that MSD is
more robust than CC for automatic ENF matching.
2.4 Existing ENF Databases
An advantage of the ENF Criterion is that one database per electric grid is
enough to establish most of the network variations for that entire grid. The
disadvantage is that a localized power outage can knock the database off-line and
cause valuable ENF information to be lost. For this reason, more ENF databases
should be configured on each grid in strategic locations. The more forensic ENF
databases that are configured appropriately around the globe will increase the
possibility to use the ENF Criterion when examining digital media. All of
continental Europe shares one electric network called the UCTE grid and this
grid is monitored by the ENF database that Grigoras configured. The United
Kingdom has an independent grid from continental Europe and the UK grid is
monitored by the ENF database that Cooper configured. The continental United
States has three grids; the Western grid is monitored by a primary database at the
NCMF in Denver, Colorado configured by Grigoras and Jenkins, and a
secondary database at the Target Forensic Services Lab (TFSL) in Las Vegas,
Nevada configured by Jenkins. The Eastern grid is monitored by the TFSL
database in Minneapolis, Minnesota configured by Jenkins and Steinhour. As of
the end of 2011 there is not a known database of Texas grid ENF. Digital media
from the UK, continental Europe, and the continental US (minus Texas) has the

potential to be compared against an ENF database. According to Rodriguez [19],
if the evidence digital media contains ENF the Criterion can still be applied to
detect edits even if no reference database is available.
Grigoras configured the first European forensic ENF database in
Bucharest, Romania in the late 1990s [1], [4], [8], [16], Since that time Grigoras
has made some changes to the database configuration. Initially, Grigoras used a
three resistor voltage divisor in the ENF probe circuitry. Eventually, the ENF
probe circuitry was updated to include the series of anti-parallel diodes to protect
the computer soundcard from network spikes and higher voltages. During the
tests performed at the NCMF the best component values were determined and
this became the modem ENF probe [22],
Grigoras sampled the ENF signal at 120 Hz initially, but as he developed
the zero-crossings extraction method, higher sampling rates were required. This
is the reason that high-resolution ENF databases should sample audio at 6 kHz -
8kHz. The model that is used in Grigoras database influenced the model that the
NCMF uses in Denver, Colorado. The Bucharest database utilizes an ENF probe
to step the 240 VAC 50 Hz signal down to a 6 VAC 50 Hz signal. The signal
passes through a three resistor voltage divisor and then a series of anti-parallel
diodes limits the amount of voltage that can pass to the output. The output of the
ENF probe is connected to a computer soundcard and then the system captures
the ENF signal at 8 kHz. The files are backed-up continuously and then
processed as needed. The acquisition computer is connected to an
Uninterruptable Power Supply to protect against short-term electric network
Alan Cooper of the Metropolitan Police in England also maintains a
Forensic ENF database. The United Kingdom electric grid is independent of
continental Europe but is also a 50 Hz grid. Using bespoke software based on
STFFT and quadratic interpolation, designed from the ground up in MATLAB,
Cooper digitizes the ENF signal by first utilizing a step-down transformer that
reduces network voltage to a safe level for a high-quality 16-bit computer
soundcard. Then, after the appropriate software interface processing, the ENF
signal is estimated using STFFT and quadratic interpolation procedures that can
be found in Coopers 2009 International Journal of Speech Language and the
Law article [13], The incoming signal values are saved to a .MAT file along with
the time and date of creation. Atomic radio clocks are used to synchronize the
acquisition systems clock to BIPM time. Coopers current ENF database
superseded his original database configuration in 2008. In the original

Metropolitan Police ENF database, Cooper developed a clever method to
overcome the time and frequency resolution tradeoffs that were inherent with the
off the shelf FFT analyzers. By using the non-linear ENF signal and the even-
order harmonics, the 100th harmonic of the ENF fundamental was used to
monitor the electric network variations. The nominal ENF in the UK is between
49.5 Hz 50.5 Hz, the 100th harmonic is 5 kHz and was expected to fluctuate
between 4950 Hz 5050 Hz because 49.5x100 and 50.5x100 create that range.
Thus a higher order FFT could be applied to a frequency range of 100 Hz instead
of a 1 Hz range, and this in addition to zero padding the time domain values
created accurate peak frequency estimations. After each FFT, the peak values
were selected from a 1.5 second window, creating a vector of peak frequency
estimates. The estimated values were then interpolated back down to 50 Hz and
the database archive was created with 100 times as many frequency estimates for
a given time period [11],
The NCMF maintains the primary US Western grid forensic ENF
database. The NCMF database is comprised of two independent acquisition
computers that run in parallel. Each computer is fed the ENF variations from
separate ENF probes plugged into the sound cards. Each computer is plugged
into a UPS and the recording software is offset by 12-hours so that PCI changes
files at 23:59 and PC2 changes files at 11:59. Each computer has its own atomic-
radio clock that synchronizes the systems time with the NIST WWVB radio
station in Fort Collins, Colorado [22],
The TFSL in Las Vegas, Nevada maintains the secondary US Western
grid forensic ENF database. This database also utilizes a dual acquisition system
offset by 12-hours. Each acquisition computer has its own ENF probe that is
feeding the computers sound card. The files are written to a Solid State Drive and
the OS is contained on a separate Hard Disk Drive. The TFSL has their own
internal network which is protected by firewalls inside the larger Target network
that is also protected. TFSL utilizes NIST NTP to synchronize the acquisition
system clocks. UPS units are in place to prevent loss of power and to provide
continued recording during a power outage.
The TFSL in Minneapolis, Minnesota maintains the primary US Eastern
grid forensic ENF database. This database also utilizes a dual acquisition system
offset by 12-hours. Each acquisition computer has its own ENF probe that is
feeding the computers sound card. The files are written to a SSD and the OS is
contained on a separate HDD. The TFSL has their own internal network which is
protected by firewalls inside the larger Target network that is also protected.

TFSL utilizes NIST NTP to synchronize the acquisition system clocks. In section
3.2 the recommendation is made to use atomic-radio clocks or GPS time
receivers but because of the unique scenario that the TFSL internal network is
configured a decision was made that security risks were negligible. This decision
was based off of a rigorous penetration test by Target Information Security
Services. UPS units are in place to prevent loss of power and to provide
continued recording during a power outage. Appendix B outlines this database
configuration in detail.
2.5 Further Directions for the ENF Criterion
The fundamental foundations for the ENF criterion have been tested,
verified, and confirmed in Europe, the United Kingdom, the United States, and
Canada. The ENF criterion has been accepted into court rooms in the United
Kingdom, Romania, Poland, Cyprus, Denmark, and the International Centre for
Settlement of Investment Disputes (Washington D.C., USA) [72]. The ENF
criterion has stood up against peer review in the Audio Engineering Society,
Institute of Electrical and Electronics Engineers, International Journal of Speech
Language and the Law, Institute of Forensic Research, and the Forensic Science
International. The ENF criterion has been adopted by the Forensic Speech and
Audio Analysis Working Group and incorporated into a forensic best practice in
Europe. In academia, a thesis from Nottingham Trent University focuses solely
on ENF and ENF research is carried out at the NCMF. The ENF Criterion has
been widely accepted across the forensics community. The forensics community
spans across international borders; national laws on the other hand, do not span
international borders.
In order for the ENF criterion to reach its full potential in the United
States it must have best practice guidelines in place from US scientific working
groups such as the Scientific Working Group on Digital Evidence (SWGDE). In
addition, the ENF Criterion must be accepted into US courts by satisfying
Daubert or Frye standards by demonstrating that the theory or technique is
falsifiable, refutable, and testable; the basis of the experts opinion has been
subjected to peer review and publication; the techniques used in arriving at such
conclusions have a known or potential error rate; there are existing methods and
maintenance of standards and controls concerning the operation of those methods
or techniques; or the theory and technique is generally accepted by a relevant
scientific community. The ENF Criterion meets the Daubert and Frye
requirements. The ENF criterion has been used by law enforcement and forensic
experts outside of the laboratory, which demonstrates that the ENF Criterion is

falsifiable, refutable, and testable. The ENF Criterion has been published in
several peer reviewed scientific journals, which demonstrates that the ENF
Criterion has been subject to peer review. The ENF Criterion has known error
rates for automatic search algorithms and the spectrographic extraction method
has a negligible error rate for low noise evidence recordings, which demonstrates
that there is known and published error rates for the ENF Criterion. The ENFSI
working group maintains a best practice manual for the ENF Criterion in Europe
and it is logical to assume that soon SWGDE will maintain a best practice
manual for the US, which demonstrates that there are maintained standards for
the ENF Criterion. There have been several scientists in the forensics community
from around the world that have researched, used, and verified the ENF
Criterion, which demonstrates wide acceptance of the ENF Criterion among a
relevant scientific community. Since the implementation of the secondary US
Western grid ENF database the Criterion can now go through cross validation
tests for databases sharing the same grid, which will assist in the maintenance
and control of standards and methods surrounding the ENF Criterion.
Further research into ENF probe circuitry is being conducted that will
advance the capabilities of the ENF probe to include hardware low-pass filters,
radio technology for broadcast purposes, and internal data storage. Examination
techniques are continually being updated and clarified, such as automatic
database search algorithms. Statistical research is being carried out to help
develop a better understanding of electric grid variations, probability of
repetitiveness, and the effect lossy compression algorithms have on ENF signals.
ENF research is fostering further development and potential; ENF will continue
to be on the cutting edge of forensic research and forensic examination on media

3. Investigating the Forensic ENF Database Configuration for use in Digital
Media Authentication
In this chapter, fifteen areas will be investigated to discover the methods
in which an advanced and robust high-resolution ENF database can be
configured to satisfy forensic standards such as accuracy, reliability, and
reproducibility. These areas have been researched through tests conducted at the
NCMF and Target Forensic Services Lab (TFSL) and have been determined to
be of importance for anyone wishing to build an ENF database for forensic
purposes. For anyone wishing to use an ENF database for authentication of
digital media, the first step to ensuring that their database meets forensic best
practices is to respect the guidelines that are explained in a variety of literature
on ENF research. Many of the available research articles focus on the methods of
applying the science behind ENF but briefly discuss configuring the entire ENF
database system. This chapter will take an investigative approach into the
configuration of an ENF database and expand on the advantages and
disadvantages of different configurations. Disrespecting recommended
configurations could diminish database security, integrity, and reliability;
eventually leading to erroneous findings, faulty science, and judicial error. To
maintain accuracy, reliability, and reproducibility the areas outlined below have
been identified as important and necessary guides for a forensic ENF database
from functionality, security, redundancy, and administrative points of view;
which combined, take into account the precautions for ultimately examining the
evidence, interpreting the results, and preparing a report for use in the court room
as well as advancing scientific research for peer-review.
The areas outlined below are not listed in order of priority and each one
should be considered just as important as the others. Each area has an impact on
the integrity of the database. Each area can affect database security, database
functionality, database reliability, or a combination of these foundations. The
fifteen areas explained below are: The NCMF ENF probe; atomic-radio
clock/source clock synchronization; sampling frequency, advantages of high
resolution ENF databases and resolution/FFT settings; sound card, input level,
and signal to noise ratio; type of storage (HDD vs. SSD); Direct Current (DC)
bias and Frequency bias; distortions; network failure/Elninterrupted Power
Supply (UPS) and safe guards; advances in ENF database configuration; and
other areas to pay attention to.

3.1 The NCMF ENF Probe
At the core of most ENF databases there will be an ENF probe. The ENF
probe is a small and simple device that receives the US 120 VAC 60 Hz (UCTE
240 VAC) signal and outputs a signal that is safe to plug into a computer sound
card for recording ENF variations and compiling ENF database files. Due to the
inherent differences in electronic components, building multiple ENF probes to
create multiple databases with matching waveforms can be challenging. This
challenge was addressed and solutions were offered [22], By using MATLAB it
was possible to determine the proper combination of ENF probe components for
the NCMF and TFSL database configurations. Time, effort, and money were
saved by using software to determine the proper combination of components to
build a high-quality ENF probe that accurately records the ENF variations from
the grid to the database. In Figure 2, the darkest line represents the calculated
waveform that was obtained from using the proper components for the ENF
probe circuitry utilized in the NCMF and TFSL forensic ENF databases.
One schematic has been proposed by Rich Sanders of the NCMF [12].
Sanders schematic was based on a voltage divisor with three resistors. Since that
time, tests have been performed using various schematics at the NCMF and
conclusions were made that a newer and more robust schematic was necessary in
order to protect the computer soundcard from network voltage spikes and to
capture ENF without clipping the waveform. Several variations of the ENF probe
circuitry were simulated and experimented with to determine the components
that will output a waveform free of clipping and distortions. The main concept of
the ENF probe is to take an electric grid signal from any wall socket and step the
signal down to a line-level signal that is safe to plug into a soundcard. This is
accomplished by using a transformer that converts the mains US 120 VAC
(UCTE 240 VAC) to 6 VAC. Next, the signal is divided through a three resistor
voltage divisor. Then, to protect the computer soundcard against possible
network spikes or higher voltage levels a series of anti-parallel diodes are used at
the probes output (see Figure 3).
When executed on the US 120 VAC Western grid, the output voltage of
the NCMF ENF probe was ~550 mV with an impedance -280 ohms relative to
the sound card used. This was accomplished by using the following component
values: R1 & R2 = 1.5 kQ, R3 = 200 Q, Dl, D2, D3, & D4 = 1N5863. When
building an ENF probe it is recommended to first simulate the ENF probe
schematic and determine the components that will best suit the needs of the
system that the ENF probe is intended to be used in.

ENF Probe Simulation
Figure 2 Probe Output Waveform
Figure 3 Proposed Schematic for ENF Probe

Another solution is to replace R3 with a variable potentiometer which
will allow the user to calibrate the ENF probe to best suit their configuration. An
ENF probe with variable amplitude was constructed at the NCMF by Jenkins and
Scott Anderson and tested to confirm the findings of the software simulation.
Through this experiment it was decided that the variable potentiometer can be
used to calibrate the ENF probe to function optimally with a wide variety of
sound cards or other components. An optimal calibration shall be one that results
in a waveform that is not too low in amplitude nor too high in amplitude and also
free of clipping and distortions. High/low amplitude, clipping, and distortions are
factors that can cause challenges when attempting reproducibility, automatic
database searches, and zero-crossings extraction.
The implementation of the ENF probe shall be carried out by connecting
the ENF probe directly to the wall socket and not the UPS, power strip, or surge
protector. By connecting the probe directly to the wall socket, one will eliminate
the possibility of the US 120 VAC (UCTE 240 VAC) signal being processed
through any power conditioning or voltage regulation circuit before reaching the
probe. Such circuits can commonly be found in UPS units, power strips, and
surge protectors. Another reason to connect the probe directly to a wall socket is
that in the event of network interruptions there will be no ENF signal but the
probe will unnecessarily draw power from the UPS, causing the database to
record the battery variations of the UPS. If the probe is directly connected to a
wall socket when the network returns to normal operation, the probe will
automatically be initiated and continue sending signal to the soundcard. As long
as the UPS is able to power the PC during the network interruption the entire
system should remain record enabled and continue recording when the network
returns to normal operation. Figure 4 shows a completed NCMF ENF probe with
variable amplitude.
There is still room for advances in the ENF probe, such as, on-board Low
Pass Filters (LPF), on-board Analog to Digital (A/D) Converters, wireless
transmission (see chapter 4), and on-board storage. For example, Figure 5
displays a graphical tool that can be used to help determine the optimal
components for an on-board Low Pass Filter (LPF). Using an on-board LPF can
help mitigate aliasing distortions in case the sound card LPF is not functioning
properly. In Figure 5 the top graph displays 0.2 seconds of a 60Hz sine wave
resulting from the component values in Figure 3. The amplitude of the waveform
from the schematic with the added resistor and capacitor has been attenuated
slightly but this can be adjusted at R3. This way, the amplitude can be checked to
ensure that it is not too low in amplitude nor being clipped by the diodes.

Additionally, a variable resistor can be used in place of R3, or different
component values can be experimented with to optimize the waveform
amplitude. In the middle graph of Figure 5, the Long Term Average Spectrum
(LTAS) of an ENF database file is displayed. The first spike indicates the 60Hz
fundamental and the other spikes are harmonics of that. The X-axis spans from 0
Hz to 4000 Hz because this file was sampled at 8 kHz meaning that there are no
values above fs/2 or 4000 Hz. The bottom graph in Figure 5 displays the same
ENF database file after the LPF has been applied. In this instance, the LPF was
created by adding a 12.25Q resistor and a 6.49 uF capacitor to the ENF probe
schematic presented in Figure 3, resulting in a cut-off frequency at roughly 2000
Hz, for demonstration purposes. This tool can be used to determine which
components will result in a cut off frequency at 4000 Hz or slightly below 4000
Hz. This information can be used to build a hardware version of the schematic
and be tested to verify that an ENF probe with on-board LPF will eliminate or
attenuate aliasing distortions when the sound card or software LPF is not
functioning properly.
Figure 4 NCMF ENF Probe

ENF Probe LPF Simulation
Figure 5 ENF Probe LPF
3.2 Atomic Radio Clock/Source Clock Synchronization
An atomic clock/source clock is a time controller independent of the
electric grid, capable of keeping the database system clock synchronized with an
accurate time reference that is also unaffected by electric grid activity. For a
forensic ENF database it is crucial that the time controller of the database is
synchronized with a reference source that is as accurate as possible. Any
deviation of this standard will cause the database time to fluctuate due to quartz
oscillators found in computers being unreliable when not synchronized
periodically [24] [25] and over time could result in the database providing
erroneous results and irreversible judicial errors. For these reasons it is suggested
that an ENF database be synchronized with the National Institute of Standards
and Technology (NIST) atomic time standard, known in the United States as
NIST Universal Coordinated Time (UTC NIST). There are three different ways

that an ENF database can be synchronized with the NIST time standard and these
different approaches will be discussed below as well as a brief explanation about
how the entire atomic clock system operates and how the calculated time
standard is transmitted across the United States.
UTC is a 24-hour time keeping system based on the International Atomic
Time Scale, which serves as the foundation for timekeeping around the world
[26], There are approximately 200 atomic clocks such as the NIST-F1 around the
globe in about 60 laboratories used to maintain the UTC time. The Earths Prime
Meridian (0 degrees longitude), located near Greenwich, England is where the
hours, minutes, and seconds expressed by UTC represent the time of day. All
other times around the globe are based from this starting point, moving East of
the Prime Meridian adds one hour for each consecutive time zone, likewise,
moving West of the Prime Meridian subtracts one hour for each consecutive time
zone. The Bureau International des Poids et Measures (BIPM) is responsible for
calculating the time reference for UTC. BIPM accomplishes this task by
averaging data from the 200+ atomic clocks housed in ~60 laboratories around
the globe. There are a few different types of atomic clocks used around the globe
but for the purposes of this thesis the most accurate atomic clock will be focused
on, which is located in Boulder, Colorado at the NIST laboratories.
The NIST-F1 atomic clock is known as a fountain clock because of the
way it calculates time, by filling a vacuum chamber with a gas of cesium atoms,
gently influencing these atoms to take the shape of a sphere using lasers that
simultaneously cool the atoms to near absolute zero, and directing this sphere of
atoms through a microwave cavity [27]. The lasers gently force the sphere of
atoms to rise about 1 meter, then due to the Earths gravity, this sphere of atoms
falls back through the microwave cavity and passes another laser that shines a
light onto the atoms. Any of the cesium atoms that had their atomic state altered
by the microwaves emit photons when the light hits them and a detector senses
this light and auto-calibrates the frequency of the microwave cavity until the
resonance frequency of the cesium atom is obtained around 9,192,631,770 Hz.
The standard for the duration of a second is thus defined as the amount of time it
takes a cesium atom to cycle 9,192,631,770 times. Atomic clocks are not
standard time of day clocks but rather they provide a ticking rate for time of
day clocks. When asked about the way NIST configured the NIST-F1, Chief of
the Time & Frequency Division at NIST, Dr. Tom OBrian was kind enough to
provide the following information:

The ultimate source of all time and frequency information distributed by
NIST is the NIST time scale at the NIST facilities in Boulder, Colorado.
The time scale comprises a system of about a dozen commercial atomic
clocks, which are regularly calibrated by the NIST-F1 primary frequency
standard. NIST-F1 is not a time of day clock, but is a frequency
standard that is used to provide the ticking rate for the time scale. The
commercial atomic clocks in the time scale are fairly stable, but not
accurate that is, each clocks ticking rate stays fairly constant for
weeks to months, but each of the dozen clocks may tick at a different
rate. NIST-F1 is used to provide a common ticking rate (frequency) for
all the clocks. A complex measurement system produces a real-time
average of all the clocks, based on their recent stability, and produces the
continually changing value of NIST time.
This rather complex system is needed because the worlds most accurate
frequency standards, such as NIST-F1, are too complex to operate
continuously. A clock measuring time of day cannot be off for even an
instant, or the time of day is lost. The time scale provides the continual
realization of the time of day, and continues to operate well even if one or
several of the approximately dozen clocks have failed. NIST-F1 is used to
provide the best possible frequency (ticking rate ) so that the time scale
time of day is as accurate as possible.
The NIST time scale is compared several times per day with similar time
scales across the world in about 60 timing laboratories. The
International Bureau of Weights and Measures in France (French
acronym BIPM) uses time from NIST and these other approximately 60
labs to produce the official international time, Coordinated Universal
Time, UTC. NIST and the other labs produce their own version of UTC,
for example UTC(NIST). UTC(NIST) is official US time, and the time that
is distributed by all NIST time and frequency services.
NIST sends this information from Boulder Colorado, where the NIST-F1
resides, to Fort Collins Colorado where the WWVB NIST radio antenna array
resides [28], The time reference information is sent from the Boulder lab to the
Fort Collins antenna array via Global Positioning System (GPS) Common-View
(see Figure 6). The GPS Common-View is a measurement technique used to
compare two clocks at remote distances from each other. Using a single
reference, the Common-View method directly compares two clocks to each
other. Errors from the two paths that are common to the reference cancel each

other out and the uncertainty caused by path delay is nearly eliminated [29],
NIST also maintains a number of servers around the United States that can
communicate with just about any computer that has access to the internet. These
servers also provide synchronized time from the atomic clock. The time signal
being sent from the NIST server to a remote computer via internet connection is
typically compensated with a 50ms lead time so that when the signal arrives at
the computer the marker reference is closer to the actual time instead of being
delayed by the amount of time the signal took to travel from the server to the end
user. NIST also utilizes GPS to transmit the atomic time reference to the end
user. This is accomplished through satellite communications. Radio is not the
only way that NIST transmits the time information from the atomic clock to the
end user. An ENF database can potentially be synchronized to NIST time in three
ways; by radio clock, internet connection, or GPS signal. There are advantages
and disadvantages of each system. In order to understand which method is best
suited for a forensic ENF database, these three systems will be discussed and the
considerations for each of them from a reliability and security point of view.
Figure 6 NIST GPS Common-View Satellite Communications

3.2.1 NIST Radio Synchronization
The WWVB NIST radio station in Fort Collins, Colorado broadcasts time
information for millions of radio clocks in the continental United States on a 60
kHz carrier signal using pulse width modulation where the carrier power is
reduced by 17 dB at the start of every second then full power is restored 0.2
seconds later to signify a binary 0 or 0.5 seconds later to signify a binary 1
[25], [27], [30]. Other information is contained in the broadcast such as year, day
of the year, hour, minute, second, day light savings time or standard time, leap
years, and leap seconds by utilizing binary coded decimal to convey this
information into what is observed when reading a radio wrist-watch or clock.
Even though it is a common misconception to call radio clocks atomic clocks,
due to the fact that there is nothing atomic about the radio clock, radio clocks still
keep their time based off of the ticking rate coming from atomic clocks such as
NIST-F1 and so they are commonly referred to as atomic clocks. Perhaps the
greatest advantage in using a radio clock to synchronize the forensic ENF
database time controller is that the grid variations and the radio broadcasts are
two completely separate and independent systems. Any malfunctions in one
system will not affect the other. For example, if there is a catastrophic grid
failure the radio station will continue broadcasting as normal because of the
redundant and sophisticated system of UPS units, back-up diesel generators, and
power switching mechanisms all housed on site in Fort Collins, Colorado. A
similar fail-safe system has been implemented in the NIST Boulder, Colorado lab
where the laboratory can operate at full capacity for days between fueling the
back-up generators. This means that the time keeping system will continue to
operate smoothly even while the grid is down. When the electric network returns
to normal operation the database time controller will re-synchronize to the radio
clock and the database time will still be accurate. Another advantage to the radio
synchronization method is that it does not introduce any type of security risk to
the database, where internet connectivity has the potential to.
Perhaps the biggest disadvantages of synchronizing the ENF database
time controller to a radio clock is that the radio transmissions are susceptible to
an increasing amount of electromagnetic interference especially in densely
populated areas, the radio clock must be situated in a place that the signal can be
received such as a window or a place with a clear view of the sky, and there is
only one broadcast site in the lower 48 states resulting in varying signal strength.
The path delay in the radio broadcast system is measurable but negligible. The
radio station clock is synchronized with the N1ST-F1 via the GPS Common-
View described above, once the radio station clock signal is sent through the

transmitters, antenna feed lines, and the antennas themselves there is a resulting
delay of approximately 0.000,017 seconds which means that over the course of
170,000 years this system would introduce a 1 second off set from the source
clock. In addition to this delay there is a certain amount of delay introduced by
distance of the receiving clock from the radio station antenna. In ideal conditions
the delay of the electromagnetic 60 kHz wave traveling through free air would
equate to about 10ms delay for every ~ 1,864 miles. Because these delays are so
minuscule NIST does not compensate for any transmission delays.
3.2.2 NIST Internet Synchronization
NIST maintains +/- 26 servers across the United States from California to
New York that can be connected to from almost any computer with internet
access in order to synchronize a computers clock to UTC (NIST) time [31].
Updated Server information and IP addresses can be found at
( Many Operating Systems (OS) such as
Windows, Mac OSX, and Linux allow the system clock to be synchronized
automatically at periodic intervals with an external source via the Network Time
Protocol (RFC-1305), Daytime Protocol (RFC-867), or Time Protocol (RFC-
RFC-1305 is the most popular protocol because this is the most robust
and accurate of the three. RFC-13 05 runs continuously in the background of most
OS and periodically gets updates from a number of servers. This protocol takes
an average from the servers, disregarding those that appear to be wrong, and
updates the computers system clock to the averaged value. The computers OS
receives the time stamp in UTC seconds since January 1, 1900. Some home
computers use a variation of RFC-1305 called Simple Network Time Protocol
(SNTP), which makes a single request to a single server. Likewise, the RFC-867
and RFC-868 do not do any type of averaging; these protocols make a single
request to a single server and set the computers clock to the received time.
RFC-867 includes additional information with the time stamp such as
Julian Date (an integral number of days since noon January 1, 4713BC. For
example, September 11, 2011 is 2,455,815.5). Daylight savings time or Standard
time, server health, the number of milliseconds being compensated for
transmission delay, an indication that the OS is receiving UTC(NIST), and an on
the mark indicator that tells the system what the time is when the mark is
received are also included in the transmitted information. According to the NIST

website, the RFC-868 is only used by 1% of consumers and will eventually be
phased out [31].
Out of the three ways a computer can be synchronized with NIST time
via internet RFC-1305 is the most widely used and most robust. Another
advantage of using NTP synchronization is that RFC-13 05 is not susceptible to
electromagnetic interference or varying signal strength the way that radio clocks
are. In common with each other, the radio broadcast systems, GPS broadcast
systems, and the internet broadcast systems are completely independent of and
separate from any electric network failures and safe guarded with several back-
up generators and redundant equipment. There are disadvantages to connecting a
forensic ENF database to the internet however; occasional lapses in internet
communications such as problems with trunk lines or Internet Service Provider
(ISP) servers can cause the synchronization to cease. Another disadvantage is the
overall security risk of having a forensic ENF database open to the outside world.
Having a forensic ENF database exposed to outside threats seriously diminishes
the integrity of the database. Imagine that someone with enough skill could
access the database and change the names of the files to represent a different day
or delete files all together. Internet synchronization is a two way communication
where radio and GPS synchronizations are one way communications. The third
method for synchronizing an ENF database to UTC(NIST) time is the GPS
3.2.3 NIST Global Positioning System Synchronization
NIST provides a GPS service for synchronizing computer time controllers
that is compiled from approximately 24 satellites orbiting the Earth; each satellite
has an onboard clock and keeps the onboard time synchronized with an average
from the atomic clocks located around the world [32], [33], [34], The advantages
of using a GPS system to synchronize a forensic ENF database are: Most GPS
time receivers on the market can track 8-12 satellites simultaneously, can
provide an average of date and time information from all satellites in view, can
provide time and date information in a computer readable format, and most units
produce a 1 pulse per second (pps) electrical output that can be synchronized to
within 100ns of UTC. Even though there can be some transmission path delays
due to ionosphere and tropospheric conditions, angle of the satellite to the
receiver, and hardware instabilities or inconstancies, the overall reliability of the
GPS system is impressive and statistically has shown 24-hour averaged accuracy
to within 10 ns. The graph in Figure 7 shows the 24-hour nanosecond uncertainty
for September 10, 2011. The highest uncertainty was almost 15ns but overall the

changes intra-variability was within less than 10ns. This graph was obtained
from ( The GPS system
shares a common theme with radio and internet broadcast, all these systems are
independent of the electric network.
The best way to implement a forensic ENF database time controller shall
be to create a fail-safe dual source clocking system. The primary time controller
should be the radio clock receiver and the secondary time controller should be
the GPS time receiver. In this manner the radio clock will automatically be
synchronized at set intervals and the system can be configured so that if a time
period longer than X-hours passes without a synchronization-update then the
GPS clock will automatically take over. A sophisticated system will use both
time controllers and average the time between both to synchronize the database
time. A system configured in this manner will ensure that an accurate radio
signal controls the system clock and in the event that radio signal is lost the GPS
source will ensure that continued accurate time synchronization is maintained.
The reason that it is best practice to use the radio source and GPS source is that
these two systems are one-way communications and do not introduce a security
risk like the internet transmission does.
In certain facilities, the database is surrounded by concrete walls and does
not have exposure to any windows, making it very challenging to obtain a clear
transmission signal for radio or GPS. One solution is that antenna receivers can
be connected to both the radio and GPS time receivers via long cable runs. The
antennas can be attached to the outside of the building or in a place with a clear
view of the sky and send the information to the time controller via the cable run.
Using the accurate time source from NIST or BIPM will ensure that the
acquisitions system clock is accurate with a consistent ticking rate and will help
mitigate sampling frequency offsets or frequency bias (see section 3.6).

Figure 7 NIST GPS Time Accuracy Over 24-Hours (09/10/2011)
3.3 Sampling Frequency
Sampling frequency (fs) or sampling rate is the rate at which discrete
values are assigned in the digital domain to represent a continuous waveform.
Digital audio is a binary representation of a sound waveform. In order for a
sound waveform to be stored in a digital format the waveform must somehow be
converted from its natural state as a pressure wave propagating through a
medium to a series of discrete values. The most popular method for this
conversion is called Discrete Time Sampling (DTS) where a two dimensional
matrix is used to represent sound waveforms as digital audio waveforms.
Traditionally, a waveform graph displays the X-axis as time and the Y-axis as
amplitude. In most audio editing software, the resolution of the waveform X-axis
is determined by the sampling frequency and the resolution of the Y-axis is
determined by the bit depth (time vs. amplitude). A 44.1 kHz 16 bit .WAV PCM
file for example, will be an audio waveform represented with 44,100 samples per
second on the X-axis for time and 65,536 (2A16) data points on the Y-axis for
amplitude. This popular sampling rate is a standard resolution because it

accurately reproduces the original waveform to a degree that the human brain can
interpret all audible frequencies as continuous. There are of course several
factors that come into play when the term accurately reproduces is used, such
as: quality and/or frequency response of the microphone(s), preamp noise,
aliasing error, and the list can be extended. The best way to mitigate errors in the
recording process is to use high quality components, well designed circuits, and
proper gain stages as will be discussed later in sections 3.4 and 3.7. There is also
a technical reason that 44.1 kHz has been adopted as an industry standard; the
Nyquist theorem states that the sampling frequency should be at least twice as
high as the highest frequency in the signal. Since most humans are limited from
20 Hz 20 kHz in the frequency range that they can perceive; a sampling
frequency of 44.1 kHz allows for all the audible frequencies to be reproduced
without aliasing (refer to section 3.7).
For safe measure, most sampling frequencies are decided by doubling the
highest frequency to be reproduced and adding 10% 20%. For a forensic ENF
database it is recommended in the June 2, 2009 Forensic Speech and Audio
Analysis Working Group (FSAAWG) document [14] that the desired signal
shall be sampled at twice the highest frequency plus 20%. The desired ENF
signal is around 60 Hz (50 Hz UCTE) and thus the minimum sampling frequency
shall be 144Hz (120 Hz UCTE). The recommended sampling frequency for ENF
databases however, is 6 kHz 8 kHz which will leave room for further types of
analysis without taking up an unmanageable amount of storage space. Figure 8
illustrates how a sounds waveform is turned into a series of discrete values.
Each data point falls at a certain interval along the X-axis determined by the
sampling frequency; simultaneously each data point is assigned a position on the
Y-axis determined by bit depth representing amplitude.
Implementing a proper sampling frequency for a forensic ENF database is
crucial and 6 kHz 8 kHz is the recommended sampling frequency for a forensic
ENF database. 6 kHz .WAV files will be approximately 1GB per 24-hours and 8
kHz .WAV files will be approximately 1.3GB for every 24-hours and will help to
manage storage requirements. Even though the target signal is 60 Hz (50 Hz
UCTE), sampling at 6 kHz 8 kHz allows for various types of ENF extractions
and scientific research such as zero-crossings.

1.5 -
1 *
0.5 -
0 -
-0.5 -
-1 -
-1.5 -
0.005 0.01 0.015 0.02 0 025 003 0.035 0.04 0.045 0.05
Figure 8 Continuous Waveform to Discrete Waveform
3.3.1 Advantages of High-Resolution ENF Databases
There are several advantages to configuring a forensic ENF database to
sample audio at a high sampling frequency. Having a forensic ENF database
configured to record the ENF signal at 6 kHz 8 kHz allows for various types of
ENF extraction methods and further scientific research. When the 60 Hz (50 Hz
UCTE) signal is captured at 6 kHz 8 kHz the harmonics of that signal are
captured up to 3 kHz 4 kHz. The harmonics can be useful when employing
different types of forensic analysis and scientific research. The zero-crossing
extraction methods in particular benefit from a high sampling rate because a high
resolution waveform will more precisely place the zero-crossing in the correct, or
as close to correct, place as possible.
3.3.2 Resolution/Fast Fourier Transform Settings
Resolution and Fast Fourier Transform (FFT) settings are critical
parameters for ENF extraction methods and can be affected by the sampling
frequency that the audio was captured at. The resolution and FFT settings can
drastically affect the results of the spectrographic, time-domain, and frequency-
domain extraction methods. Time and frequency are inversely related when

applying FFT. The more frequency resolution one obtains by increasing the FFT
order to 16,384 for example, the less time resolution will be available. The more
time resolution one obtains by decreasing the FFT order to 512 for example, the
less frequency resolution will be available. This is because resolution can be
defined as: Resolution (R) = Sampling Frequency (fs) / Fast Fourier Transform
(FFT) order or [16]:
For example, if the sampling frequency is 144 Hz and the FFT order is
16.384 then the resolution will equal 0.008 Hz. If the sampling frequency is 8
kHz and the FFT order is 512 then the resolution will equal 15.6 Hz. This means
that when the ENF signal is down-sampled to 144 Hz and the FFT order is
16.384 it is possible to analyze variations as small as 0.008 Hz. When the ENF
signal is left at 8 kHz analyzing frequency variations less than 15.6 Hz can be, to
put it mildly, challenging. On a technical note, analyzing the UCTE 50 Hz signal
is superior to analyzing US 60 Hz because the 50 Hz signal can be decimated
more than the 60 Hz signal due to Nyquist limitations, thus the fs becomes
smaller allowing for smaller observable variations of the frequency content.
The implementation of correct resolution/FFT settings is crucial for
proper ENF extraction methods. These areas should be tested and analyzed on a
case by case basis. Different cases or research will require different
resolution/FFT settings; but whenever ENF comparisons are being made, the
evidence and database fdes shall have the same time and frequency resolution.
3.4 Sound Card
The sound card is the interface that translates the analog signal coming
from the ENF probe into a digitized representation of that waveform. There are a
wide variety of sound cards available on the market such as PCI cards, PCI
express cards, external USB cards, rack-mount units, and the list can be
extended. The sound card is typically responsible for converting the analog
signal to the digital signal, commonly referred to as an A/D converter. Sound
cards can have a wide range of connection types from 1 /8th inch, % inch, XLR,
RCA, and the list can be extended here as well. The type of sound card or the
R =

connections for the sound card are not of great importance so long as the sound
card is capable of performing its duty as an interface in a forensic ENF database.
The sound card used in a forensic ENF database shall have the capability
to record at the desired sampling rate. The NCMF and TFSL sampling rates are 6
kHz 8 kHz for ENF database .WAV files. A wide variety of soundcards on the
market advertise the capability to record up to XX kHz, the problem is that the
soundcards may record at high sample rates but the soundcards may lack the
ability to record down to 6 kHz 8 kHz. As discussed in section 3.3, a 6 kHz 8
kHz sampling rate captures the ENF signal enough times per second to fulfill the
freedom to conduct various ENF extraction methods and scientific research
without occupying an unmanageable amount of storage space.
Whichever sound card is decided upon, it should be tested and verified to
be performing optimally, in other words, a Signal to Noise Ratio (SNR) of at
least -94 dB as well as a Total Harmonic Distortion (THD) of at least 0.003%
[16] to ensure the linearity of the original signal is not being compromised.
Testing for aliasing, jitter, and other distortions should be conducted before the
ENF database is operational. Software can be used to sample the signal instead of
a hardware A/D converter but the software should be verified to be producing
valid results through testing for aliasing, jitter, and other distortions. The
software should also be able to sample at the desired sampling frequency of 6
kHz 8 kHz. In section 3.7 distortions such as aliasing are discussed that can
result if the soundcard/software is not low-pass filtering the signal correctly.
3.4.1 Input Level
Input level is the amplitude at which the ENF probes output is received
when entering the acquisition system. The input level of the signal can also affect
the integrity of the database by being too high or too low. The Input levels of
recorded ENF signals at the NCMF are ~ -12 dB Full Scale (FS). This allows for
a strong signal without clipping. Noise can also have an effect on zero-crossings
and the input line should not be introducing noise. All electronic and electric
systems are going to inherently introduce some level of noise. Depending on the
type of noise being introduced it could add unwanted frequency components or
could change the original signals linearity; such as Total Harmonic Distortion
(THD). By using high-quality sound cards and high quality circuits it is possible
to obtain low THD interference and avoid altering the relationship between
voltage and current of the original signal. 0.003% THD is acceptable [16].
Unwanted frequency components can be added to the original signal in many

ways including poor circuit design, low-quality components, and improperly
designed cables. The more leads, coils, and inductors that are introduced along
the signal path the more potential there is for introducing unwanted noise into the
database. In Windows 7 OS the configuration of input source and level can be
modified through the Control Panel. RecAll Pro also has a tab under Preferences
that allows the user to modify which driver RecAll Pro uses. Proper gain stages
should be utilized to optimize the acquisitions system probe output, sound
card/interface, and
3.4,2 Signal to Noise Ratio (SNR)
The signal to noise ratio can be defined as the difference between desired
signal and unwanted noise, also referred to as noise floor. If the desired signal is
-12 dBFS and the noise floor is -108 dBFS the signal to noise ratio would be 96
dBFS. In a forensic ENF database the signal to noise ratio is crucial for correctly
implementing the extraction methods. The signal to noise ratio shall be as high as
it is possible because low signal to noise ratios will decrease the ability to
properly estimate peak frequency values potentially effecting automatic search
algorithms and decreasing important identifying characteristic detail when
applying the spectrographic method. The error rate chart in Figure 9 is coming
from Grigoras 2009 [16]. This chart shows that the lower the signal to noise ratio
of evidence recordings the higher the false alarm probability will be when
applying automatic algorithms for ENF extraction. When visual matches are
made, such as the spectrographic method, there are no reported error rates,
however the identifying particularities of the signal are diminished.
Figure 9 is a chart used for evidence digital audio recordings; by
measuring the SNR of the nominal ENF frequency against the recordings noise
floor, the chart in Figure 9 can be used to determine the false alarm probability
when applying automatic search algorithms to evidence. Basically, the closer the
signal of interest is to the noise floor the more likely one will be to obtain a false
result. Even though the forensic examiner has no control over the conditions in
which the evidence was created, the chart in Figure 9 can be helpful in assessing
the best approach to conducting an ENF analysis.
The SNR of the forensic ENF database however, can be maximized by
using high quality components and circuits that introduce less noise and less
THD as well as using a variable resistor in place of the ENF probe schematic R3,
this way the optimal signal can be calibrated for the system it is being used in.

Higher sampling rates such as 8 kHz can help in creating better resolution and be
beneficial when applying filters in post-processing.
Figure 9 Evidence Signal to Noise False Alarm Probability
3.5 Type of storage (HDD vs. SSD)
The information gathered for the forensic ENF database must be stored
for later reference. Storage media such as Hard Disk Drives (HDD) or Solid State
Drives (SSD) are examples of popular storage media. When building a forensic
ENF database the decision to use HDD or SSD will need to be made. The drives
can be configured in a variety of ways for example, a Redundant Array of
Independent Disks (RAID) can be utilized to stripe ENF data files across two
disks, the Operating System (OS) can be installed on one drive while another
drive is reserved for reading and writing only, or SSD can be used and the
combination of possibilities is almost endless. There are advantages and
disadvantages to any type of storage configuration. To investigate some of these
configurations a series of tests was conducted using different storage

A forensic ENF database will continually write files to drives 24-hours a
day, 7-days a week 365-days a year and when it comes time to move files to an
external back-up drive, the system is tasked with reading and writing
simultaneously. If the HDD RPM is too slow then errors can occur in the
database files that look similar to files that have been edited. This problem has
the potential to cause serious doubts in the integrity of the database, especially
when being presented to a judge or jury. HDDs spinning at 7,200 RPM have
been tested and determined to spin too slow for a forensic ENF database. An
experiment was conducted by simultaneously reading and writing ENF database
files to a HDD spinning at 7,200 RPM. Next, the file was processed in the same
manner database files are processed for the spectrographic extraction method
(down-sample to 144 Hz & band-pass filter around 60 Hz). Write-errors were
found that had been introduced into the file that looked similar to a file that
deletions had been made on.
Figure 10 illustrates an ENF database file that has been processed for the
spectrographic extraction method (down-sample 144 Hz & BPF around 60 Hz).
While this ENF database file was being written, other database files were copied
from the same HDD to an external HDD. The two broadband spikes correspond
to the times when the files copied, thus interrupting the write process and
introducing error into this file. These broadband spikes can also be found in
audio files that have had sections deleted from them.
Figure 10 ENF Database HDD Write-Error (7,200 RPM)

Figure 11 displays the spectrographic extraction of an ENF database file
(down-sample 144 Hz & BPF around 60 Hz). Four deletions were made in this
file which can be seen as broadband spikes throughout the recording. These
deletions look similar to the errors introduced by slow disk speed write errors.
The examiner would have a difficult time explaining to the jury why indications
of a deletion were found in an evidence file when the database used for
comparison exhibits the same particularities.
Figure 11 ENF Evidence Deletions
These types of write errors do not occur every time files are moved from
the disk that is reading and writing simultaneously. An experiment conducted at
TFSL has revealed that write errors can be generated from a variety of sources
such as Windows updates, machine maintenance processes, and random
generation. The experiment carried out at TFSL involved the use of two
independent acquisition computers. PCI was configured with a Western Digital
7,200 RPM HDD that contained the OS as well as the storage space for the ENF
files. PC2 was configured with a Western Digital 7,200 RPM HDD that
contained the OS as well as storage space for ENF database files, in addition,
PC2 was configured with a second Western Digital HDD that rotated at 10,000
RPM. The 10k RPM HDD in PC2 was used for ENF database file storage only
and there was no OS on the 10k HDD. Next, one instance of RecAll Pro was
configured on PCI to record .WAV PCM 8 kHz files and save them to the only
local drive in the computer.

On PC2 two instances of RecAll Pro were configured to record .WAV
PCM 8 kHz files and save the output of the first instance to the 7.2k RPM HDD
and save the output of the second instance to the 10k RPM drive. The systems
were allowed to run over night. The next morning, while the files were still being
written on both PCI and PC2, files were moved back and forth from the 10k
RPM HDD in PC2 to the 7.2k RPM HDD in PC2 every 15 minutes for 1 hour
and 15 minutes for a total of 5 file transfers. When the file transfers were finished
the systems were allowed to record for an additional 15 minutes. The recordings
were stopped and then analyzed to see if 10k RPM HDD rotates fast enough to
avoid write errors. The results of this experiment were not exactly the anticipated
results. PCI 7.2k RPM HDD encountered zero write errors for the 19 hour
duration that it recorded. PC2 10k RPM HDD encountered four write errors. PC2
7.2k RPM HDD encountered four write errors. Every write error on PC2 7.2k
RPM HDD occurred at precisely the same moment as the write errors on PC2
10k RPM HDD. None of these write errors occurred during the file transfer time.
These results indicate that 10k RPM HDDs are susceptible to write
errors, write errors do not always occur when files are being transferred, and the
electric network was not the cause of these simultaneous errors. The files being
transferred back and forth between PC2 HDDs were roughly 800MB in size. All
of the log files were inspected on PC2 in order to determine if there was an
update or some automatic system maintenance that caused the simultaneous write
errors, no events were logged within less than 5 minutes from a write error, (see
Figure 13). One would expect that a single ENF probe providing a single sound-
card the signal would produce the same waveform on two drives being written to
simultaneously because the A/D converter only has one output. If the waveform
in Figure 13 is examined closely it can be seen that the waveforms are not
identical, even though the input of the PC2 HDDs should have been receiving
the same bit words from the A/D converter. Figure 13 is a close up view of one
of the write errors that occurred simultaneously on two separate HDDs.
One advantage of the HDD is that they yield a lot of storage capacity for
a small price. One disadvantage of the HDD is that they have several moving
parts and the disks rotate at various speeds such as 7,200 RPM or 10,000 RPM. If
a HDD is used in the database then it is highly likely that errors such as the write
errors will be introduced. This is the reason that SSD are recommended over
HDD. In another experiment a SSD was used to simultaneously write an ENF
file and copy other ENF files from the same SDD three times during the
recording. After processing the file that was written to the SDD, no traces of

write errors were found. Other storage media configurations were experimented
with during the configuration of the TFSL ENF database, please refer to
appendix B.
Figure 12 Comparisons of Three ENF Files

3.6 Direct Current (DC) Bias and Frequency Bias
DC bias can be a product of the original analog signal, imperfections in
the A/D conversion process, or a number of other sources. In a digital system DC
bias can be the result of poor quality/design of components in the A/D converter
and if DC bias is introduced, the waveform (X-axis = time, Y-axis =
amplitude/voltage) average value will not be zero. DC bias is seen in the
frequency domain as a finite value at zero Hz. For example, if there is a DC bias
present in the signal, the amplitude of the waveform will not be centered on zero,
when the signal is averaged the result will be something other than zero. ENF
processing involves non-real time processing thus the simplest way to
compensate for DC bias is to compute the average of all the samples then
subtract this average from each sample [1], [4], [8], [16], [61]. Detecting DC bias
can be simple because the peak values of the waveform should be as equally
distant from the center line as the valley values.
Frequency bias on the other hand, can be a product of an offset or bias in
the frequency of the recording device clock. If there is an error in the recording
device clock and frequency bias is introduced, the nominal ENF frequency will
appear at some other frequency. For example, if a recording device clock is
incorrectly counting seconds then the sampling frequency will not be sampling at
the apparent setting; if the fs = 8 kHz but the recording device clock is taking 1.1
seconds to sample 8,000 times then a 60 Hz signal will have additional cycles per
second causing the 60 Hz signal to appear higher than it actually is. If frequency
bias is present in a signal then subtracting the nominal value from the waveform
(X-axis = time, Y-axis = frequency) will result in a value other than 0 Hz.
DC bias and frequency bias are particularities of the recording device and
the examiner has no control over the method used to create the evidence. The
examiner does, however, have control over the methods and precautions used in
the laboratory. Being aware of DC bias and frequency bias and the affects they
can have on ENF is an important consideration when implementing the ENF
criterion as well as when maintaining a forensic ENF database. DC bias is an
offset of an amplitude/voltage vs. time waveform average from zero. Frequency
bias is an offset of a frequency vs. time waveform away from its nominal value.
DC bias and frequency bias can both affect the zero-crossings of a signal.
If the DC bias is not mitigated properly the zero-crossings of the signal will be
too close to the bottom or top of the wave form depending on weather the DC
bias is positive or negative. This will become a difficult challenge when

attempting to apply automatic database searches based on zero-crossings because
the length of semi-periods will be too short, then too long, then too short, and so
on every time the signal crosses zero. The frequency bias can affect zero-
crossings because the signal will have lower or higher frequency than the
nominal ENF, causing the cycles to be elongated or shortened. DC bias should be
removed from evidence recordings by computing the mean of all the samples and
then subtracting this average from each sample. Frequency bias should be
removed from evidence recordings by computing the mean of the time and
frequency vectors and then subtracting the means from them. DC bias should be
removed from database recordings, and typically will be removed through the
post-processing. If frequency bias is not dealt with properly then the results of an
ENF analysis can be incorrect. For an example please refer to section 2.3
Conflicting Theories about the ENF Criterion.
3.7 Distortions
In digital audio, a distortion is any change in the content of a signal or a
change in the shape of the signal waveform during its transmission. Distortions
can come from a variety of sources and have a variety of effects on the recording.
Some distortions come from hardware such as write errors in a HDD, a
malfunctioning low-pass filter, or a bad word-clock. Other distortions come from
signals for example, poorly planned gain stages, low quality ENF probe circuits,
or low signal to noise ratio. A complete and exhaustive list of distortions will not
be covered here but only the most common and easily overlooked distortions.
Any of the distortions mentioned here are simple to test for and simple to correct.
When establishing a forensic ENF database, attention should be paid to these
types of distortions and steps should be taken to mitigate these errors before they
Digital aliasing occurs when the system tries to reproduce frequencies
that are above half the Nyquist frequency (fs/2). There are simply not enough
samples per second to reproduce the number of cycles per second in frequencies
above fs/2 since each cycle needs at least two samples per period to be
reproduced. Aliasing is a type of distortion and an important consideration when
determining sampling frequency for an ENF database. In an ENF database there
are two ways that audio can be sampled; hardware or software. Both methods
follow the same logic: Signal is received, signal is low-pass filtered to respect the
Nyquist Frequency, signal is sampled. If there is a problem with the initial low-
pass filter then frequencies above fs/2 will enter the sampler and start to
introduce a variety of errors. Figure 14 illustrates a 60 Hz sine wave sampled at 8

kHz. Figure 15 illustrates what aliasing can do to a signal, in this case a 60 Hz
sine wave sampled at 110 Hz, showing how the distortions can affect a signal.
Another consideration about aliasing is the effect of fold-over where
frequencies beyond the Nyquist frequency begin to fold-over into the audio
bandwidth. This fold-over effect is caused by improper sampling rates,
dysfunctional LPFs, or other sources and has detrimental effects on the quality
of the recorded audio. The problem with fold- over is that the aliasing distortions
being introduced are indistinguishable from real frequencies by the recording
system. For example, if the sampling rate is 8 kHz and a signal of 6 kHz is
introduced then there will be a fold-over of 2 kHz where 8 kHz =fs (Sampling
Frequency), 6 kHz = F (Frequency > fs/2), and 2 kHz = F/ (Aliasing
Figure 14 60 Hz Signal Sampled at 8 kHz
To demonstrate what can happen to the signal if the low-pass filter is not
working correctly an experiment was conducted using a hand-held digital
recorder, a laptop sound card, and the Sage Brush RecAll Pro recording software
and illustrated aliasing errors and their effect on the audio spectrum when the
Nyquist Frequency is not respected. To Begin, a sine-wave sweep was generated
from 20 Hz 20 kHz that repeated every 10 seconds from a laptop. Next, a Sony
PCM D-50 was configured to sample at 22 kHz and the recorder was connected
to the laptop. The anticipated results were that any signal above 11 kHz would
start to show signs of fold over. To check the outcome a spectrogram was used

with 1024 256 FFT resolution (respectively) and the results were observed in
both linear and logarithmic scales.
Figure 15 60 Hz Signal Sampled at 110 Hz
fs F = F/
The linear spectrogram in Figure 16 is the result of a sine-wave sweep
from 20 Hz 20 kHz that is not cyclical but repeating, rising from 20 Hz to 20
kHz and then starting over at 20 Hz. This is a magnified view of one sweep over
the course of 10 seconds. The X-axis is about 10 seconds long, the Y-axis is
about 11 kHz high, and light areas indicate high amplitude. The sine wave sweep
was recorded at 22 kHz 16 bit .WAV PCM. In this linear view of the
spectrogram (1024 FFT) it can be seen that the low-pass filter of the Sony PCM
D50 is not functioning properly. If the low-pass filter were working correctly
then there would not be any fold-over. The system is taking the frequencies
above fs/2 and interpreting them as frequencies that are within the fs/2 bandwidth
by applying the 22 kHz sampling frequency to frequencies that are cycling faster
than 11 kHz per second. For each frequency above fs/2 the system interprets that
as a frequency proportional to the difference between cycles per second and fs/2.
For example, 12 kHz in this instance is 1 kHz above fs/2 and is interpreted as a

frequency 1 kHz below fs!2 and is introduced back into the bandwidth becoming
indistinguishable from the original 10 kHz frequency. The light areas to the left
of the strong signal are the harmonics and rise at steeper angles because there are
more frequencies between higher tones than there are between lower tones. For
example, there are 1,000 Hz between 1 kHz and 2 kHz and there are 100 Hz
between 100 Hz and 200 Hz; even though 1 kHz 2 kHz and 100 Hz 200 Hz
are both octave ranges.
Figure 16 Sony PCM-D50 Sampled at 22 kHz
In Figure 17 the same 20 Hz 20 kHz sine wave sweep was used, the
difference is that the sweep was sent through a laptop sound card and recorded
with RecAll Pro. The sampling frequency and length of time are the same as
Figure 16. The FFT resolution is the same as Figure 16 (1024) and this is also a
linear scale.
However, in Figure 17 the aliasing frequencies being folded over back
into the audio bandwidth are more apparent, having higher amplitudes, thus
causing further distortions of the original signal. There is also more background
noise in this recording, seen here as a light haze at the bottom of the spectrogram.
This background noise could be introduced by a noisy soundcard, transmission

noise picked up along the signal path, or other sources. Even with the
background noise taken into account, the increase in aliasing distortions is being
caused by an inferior quality soundcard that is not applying a good low-pass filter
to the signal entering the sampler. The same effect of the harmonics to the left of
the strong signal can be seen in this example, as well as new generations of
aliasing distortions. Because of the increase in amplitude of these distortions it
can be said that an un-quantified amount of error will be introduced into the ENF
database inversely proportional to the quality of the low-pass filter. In other
words, the lower the quality of the low-pass filter, the higher the amount of
distortions and error being introduced to the database. Likewise, the higher the
quality of the low-pass filter the lower the amount of distortions and error are
introduced to the database.
Figure 17 RecAll Pro Sampled at 22 kHz
In Figure 18, the same 20 Hz 20 kHz signal sweep was sent through the
laptop sound card and recorded using RecAll Pro. The sampling frequency has
been changed to 8 kHz and the FFT resolution has been changed to 256 to
compensate for the lower sampling rate. The scale in this spectrogram is also
linear and the X-axis runs for about 10 seconds and the Y-axis is about 4 kHz
high. The biggest consideration to take into account with Figure 19 is that the

signal being sent into the sampler contains much higher frequency content than
the sampling frequency, in fact the sine-wave sweep signal produces frequencies
2.5 times higher than the sampling rate which means that the effects of aliasing
will be four-fold, literally. The fold over of frequencies ranging from 4 kHz 8
kHz is seen in the aliasing distortions that range from 4 kHz 0 Hz in the audio
bandwidth. For the frequencies that range from 8 kHz 12 kHz, the aliasing
distortions fold over again and range from 0 Hz 4 kHz. For the range of
frequencies between 12 kHz 16 kHz, the aliasing distortions fold over a third
time and range from 4 kHz-0 Hz. Lastly, the frequencies ranging from 16 kHz -
20 kHz are folded over a fourth time and range from 0 Hz 4 kHz. This is the
reason that the zigzag pattern starts to appear in this spectrogram with a linear
scale. To make things even worse, there are all the harmonics to the left of the
strong signal that further compound this error and add an increasing amount of
distortion to the database. This is a specific reason not to rely on the recording
software to low-pass filter signals unless the software is tested and known to
resolve this issue.
Figure 18 RecAll Pro Sampled at 8 kHz
For implementing an aliasing free database it is recommended to verify
that the sound card and/or software are applying a suitable low-pass filter. If

aliasing occurs, the distortions will introduce an un-quantified amount of error
into the results. The aliasing distortions will introduce frequencies that are
indistinguishable from the original signal making it impossible to determine the
correct 60 Hz (UCTE 50 Hz). For forensic best practices aliasing distortions
should be avoided.
There are other types of distortions to be mindful of, such as distortions
introduced by the components of the ENF probe. The ENF probe components in
section 3.1 were selected after conducting a series of tests at the NCMF of
different component values and looking at the graphical results to determine
which components caused the signal to be too low in amplitude, in a good range
of amplitude, and too high in amplitude. The components that caused the signal
to be too high in amplitude were avoided because signals that are too high in
amplitude can cause the signal to be clipped. Another solution is to use a variable
resistor in place of R3, this way the probe can be calibrated to best suit the
system it is being used in. Changing the values of the diodes can allow more or
less voltage to pass, but for the configuration at the NCMF the best components
for the NCMF system were presented in section 3.1 and in the AES 131st
Convention Paper [22].
Clipping occurs when components are overdriven by voltage and the
resulting waveform is flattened at the peaks and valleys. This type of distortion
for an ENF database is unacceptable and should be avoided by using a well-
designed ENF probe built with high quality components. The anti-parallel diodes
in the modem ENF probe allow 1.4V to pass, +.7V and -,7V. Any voltage
beyond that will result in a clipped waveform, in respect to section 3.1. In Figure
19, the resulting waveforms of various component combinations are illustrated.
The Darkest line represents a clipped waveform coming from components that
were over-driven and had content clipped by the anti-parallel diodes. The
information that is clipped cannot be readily recovered. This is a problem for a
forensic ENF database because this type of distortion affects several parameters
in the ENF extraction process such as harmonics, spectrographic analysis, and
automatic searching. Most importantly the zero-crossings cannot be estimated if
the peaks and valleys are clipped. To help determine the most suitable circuit for
a clean waveform Table 1 was created with different valued component
combinations. The values in Table 1 correspond to the graph in Figure 2 and
Figure 19. Referencing the schematic presented in Figure 3, the values for R3
and the sound card impedance were changed. Combination number 9 had the
highest clipping and is represented in the Figure 19 as the dark sine-wave. A
distortion free output waveform with strong amplitude is represented as the dark

sine-wave in Figure 2. For implementing a forensic ENF database that is free
from clipping distortions the NCMF configured the ENF probe circuit in the
following way: The transformer steps the US 120 VAC (UCTE 240 VAC) down
to 6 VAC and R1 & R2 = 1.5 kd, R3 = 200 £2, Dl, D2, D3, & D4 = 1N5863. An
LED was also placed in the circuit so that when the ENF probe is functioning
properly the LED will be lit.
Signal Distortion
Time [s]
Figure 19 Distorted Signal
Another type of distortion to be mindful of is called jitter. Jitter occurs
when the system word-clock is not synchronized properly, resulting in erroneous
zero-crossings. The system determines the bit value based on signal and if noise
is being introduced that is close in amplitude to the signal then the system may
assign a 0 for a bit when it actually should have been a 1 or vice versa. This
sort of error will cause the signal to have zero-crossings that do not actually align
with the original voltage. There are two common ways to check the word-clock:
Peak to Peak and RMS. In the peak to peak method the distance between
consecutive peaks of a sinusoid are measured against a pure sinusoid to
determine if the word-clock is accurate. In the upper waveform of Figure 20 is
the 60 Hz signal coming from an ENF database file. In the lower waveform is a
60 Hz sine wave generated from a tone generator. By measuring the peak to peak

in the pure-tone, jitter can be determined. The peaks of the pure tone should be
spaced equally; if the peaks are not spaced equally then the word-clock is either
advancing or delaying the signal, which in turn will start to affect the zero-
crossings. In the ENF database file, it should be expected that the peaks will not
be spaced evenly even if jitter is absent because ENF is a variation around 60 EIz
(UCTE 50 Hz) and is not a pure-tone. However, if the word-clock accuracy and
performance can be determined to be satisfactory with a pure-tone then the ENF
signal will also be represented accurately. To verify that the pure tone is being
accurately captured: 1 second can be divided by 60 Hz (1/60 = 0.016). By
measuring the distance from peak to peak in the pure-tone and comparing that
distance to the time line, the distance from one peak to the next or one cycle is
0.016 seconds. This process is repeated thousands of times on several seconds or
minutes of audio.
For a forensic ENF database to be implemented properly, jitter should be
taken into consideration and measured. Tolerances for jitter can vary, but 10 Hz
in 1 second or a standard deviation of 1,000 waveform cycles is considered low
jitter by the International Telecommunication Union series G document:
Transmission Systems and Media ITU-T G.810 classifications from August 1996
[62], which might be good enough to keep from dropping packets of data but
forensic ENF databases may require tighter controls.
Figure 20 Peak to Peak Jitter Measurements

In the Root Mean Square (RMS) method the jitter can be obtained by
calculating the square root of the mean of the squares of the magnitude values of
a signal. With a sinusoidal signal the square of the magnitude at a given
resolution can be calculated, the mean of the resulting values can be calculated,
and then the square root of that mean can be obtained, resulting in the RMS of a
signal. In plain English, the RMS is calculated by adding all the squares of the
discrete values and then dividing that number by the number of samples and then
taking the square root of that. A simple example would be:
(xiY + te)2 + (*3)2 + + (.xnY
The RMS obtained from the system can be compared with the RMS of an
ideal clock to see the jitter difference. When making a jitter assessment of the
system having an oscilloscope, an ideal clock, and other specialized tools can be
helpful, but expensive. To implement a forensic ENF database that has a
tolerable amount of jitter, the system clock should be checked periodically to
ensure that zero-crossings are not being affected to a degree that will return
erroneous results.
Table 1 Tested ENF Probe Component Values
# R3 SC impedance Vo (mV)
1 black line 200 Ohms 100 Ohms 130
2 black dash 300 Ohms 100 Ohms 146
3 black dot 400 Ohms 100 Ohms 155
4 black line 200 Ohms 1 kOhms 315
5 black dash 300 Ohms 1 kOhms 428
6 black dot 400 Ohms 1 kOhms 520
7 black line 200 Ohms 10 kOhms 368
8 black dash 300 Ohms 10 kOhms 530
9 black dot 400 Ohms 10 kOhms 681

3.8 Network failure/Uninterrupted Power Supply (UPS) and safe guards
Network failure is commonly known as a power outage, where an
interruption has occurred between the produced power and the consumed power.
Network failure can be caused by many factors such as a bad transformer,
disproportional consumption versus production, or a problem at the production
source. Network failures can affect small localized areas like a few city blocks or
network failures can affect much larger areas like a large portion of the entire
grid. On September 9, 2011 two nuclear reactors lost electricity affecting the
entire South Western portion of the US Western grid, leaving 5 million people
without power until the system could be restored to normal operation [63], On
March 11, 1999 the largest network failure in history occurred in Sao Paulo,
Brazil and surrounding areas [64], A lightning strike initiated the Brazilian
network failure that eventually left an estimated 75 to 97 million people without
power until the system could be restored to normal operation. The span and
duration of network failure can vary widely based on a number of factors and the
way the network is configured. There is some protection against small, localized
power outages for a forensic ENF database as will be discussed here.
An Uninterruptable Power Supply (UPS) is a device that will keep
equipment powered during short network failures. The UPS is basically a battery
that holds a charge capable of powering computers or other devices for short
periods of time typically between 15 minutes and an hour. The UPS will keep a
forensic ENF database recording during the outage and when power is restored
the database should start recording the signal automatically. If the network
failure lasts longer than the UPS can maintain the equipment then the ENF
database will need to be restarted manually.
Certain facilities may use back-up generators to supply the building with
power until the network returns to normal operation. In this circumstance, if the
network fails, the UPS would keep the database computer active until the
generators took control. Once the generators take control of the power to the
building the ENF database would start recording the signal from the generator
which will serve little forensic use. The database will continue to record the
signal from the generators until the network returns to nonnal operation and the
generators give control back to the network. Depending on the backup generators
configuration, the generators may keep control of the buildings power for a
duration of time after the grid is functional. Valuable ENF data would be lost at
this point.

This is a situation where localized power outages can be detrimental to
the database. Some simple solutions would be: have multiple redundant
databases at strategic locations on the same grid so that localized network
failures would not decimate the entire database information for that grid, like the
Denver and Las Vegas ENF databases used to monitor the US Western grid ENF.
As far as recording generator signal after the grid is back online, a simple
solution would be to connect the ENF probe to a socket that is powered only
from the grid and has no connection to the generator breakers.
To implement the UPS in a forensic ENF database, the primary and
secondary database computers should have their own UPS and only the primary
and secondary database computers should be connected to a UPS. The computer
monitors, ENF probes, and any other peripheral equipment should not be
connected to the UPS because unnecessary power will be drawn from the UPS,
essentially shortening the life span of the battery power supply during electric
network interruptions. The ENF probe should be connected directly to the wall
socket and not connected to a power strip as some power strips may introduce
power conditioning or power regulation to the signal. The ENF probe will
automatically shut off when the network fails but the computer and the recording
software will continue to record. When the network is restored to normal
operation the ENF probe will automatically start sending the signal to the
computer again and the database recording will continue. See appendix A for
more information about ENF probes connected to voltage regulated circuits.
3.9 Advances in ENF database configuration
Since the time Dr. Grigoras established the first ENF database there have
been advances in the configuration of the database to meet forensic best practices
[11], [12], [13], [17], [20], [22], [23], The advances used in the NCMF and TFSL
forensic ENF databases are discussed here to establish a more robust forensic
ENF database from the software used to the minimum requirements to fulfill the
extraction needs. Using audio recording software (Sagebrush Record All)
automated recording of the ENF probe output can be set up. Different options
can be set such as timing for automated file saving, file type, and sampling
frequency. Automated file saving can be useful for keeping the maintenance
requirements of the database at a minimum and also to keep the file structure
consistent, as it is presented in Figure 21 (the proposed Sagebrush Record All
settings). Uncompressed .WAV PCM files are the format of choice to avoid
compression in the ENF database files, as shown in Figure 22.

1 General || Wave || Mp3 || File j| Record Play Back
Timer | Vox | Driver Switches HotKey
Event Time New file Prefix
1 00:00-23:59 daily X
0 Enable timers New Delete Edit

OK Cancel Help
Figure 21 RecAll Pro Timer Settings
Timer 1 Vox j| Driver | Switches Jj HotKey
| General Wave Mp3 File || Record |j Play Back
0 Use WAV as default audio recording format
Resolution Channels
O 8-bit 0 Mono
016-bit O Stereo
Default Sample Rate [8000 v
Default Compression
No compression V
Default Byte Rate 0.916 MB/'min
Figure 22 RecAll Pro Audio File Settings

While various ENF database configurations and methods to extract ENF
are presented by Grigoras [1], [4], [8], [16], [22]; Sanders [12]; Cooper [11],
[13], [23]; and Micha£ek [17], using a high sampling frequency generates much
more information per second about the recorded audio and creates a higher
resolution database than sampling at twice the nominal frequency plus 20%,
which will be helpful later when employing different methods of ENF
comparison. Most importantly, it is necessary to acquire ENF signals at a higher
sampling frequency for applying the zero-crossings method of frequency
estimation where high resolution is critical [1], [4], [8], [16]. Sampling the ENF
signal at 6 kHz 8 kHz will require 1GB 1.3GB storage per 24 hours which is
a manageable amount of data, in addition, the stored file can always be down-
sampled to any desired sampling frequency.
The minimum ENF database settings used at the NCMF and TFSL are:
.WAV PCM uncompressed files, 6 kHz 8 kHz sampling frequency, 16 bit,
mono. Recording one 24-hour file per day with these settings results in ~1GB -
1.3GB of data per file. In order to extend the scientific research possibilities on
ENF, including a cross verification of different methods to check evidence
against an ENF database, a complex acquisition and analysis system is suggested
and illustrated in Figure 23 and Figure 24 and described below.
It is recommended to employ two independent ENF acquisition systems
and multiple levels of data backup. This redundancy is necessary for validating
data and to accommodate for system updates, maintenance, and reconfiguration.
As an additional precaution acquisition computers are powered by a UPS (see
section 3.8). This will ensure that during a power outage the computers will
continue recording. Even though the recorded signal during the power outage
will be zero, when power returns, the computers will still be recording. The ENF
probes are not connected to the UPS in order to collect unconditioned and
unfiltered power. The recorded .WAV PCM audio files are saved to an external
HDD for further digital signal processing including down-sampling of data and
the calculation of zero-crossings and short-term FFT values. Processed ENF data
such as down-sampled audio and frequency values from both ZCR and FFT
calculations are considered sub ENF databases and should be stored remotely
either through a network hard drive or at a separate physical location. This is
detailed in FIGURE 25.
The primary and secondary database computers should have their time
synchronized with both a radio clock and a GPS time receiver for each
acquisition computer (see section 3.2.3). The radio clocks receive information on

the 60 kHz radio band in the United States and the GPS time receiver utilizes
GPS common-view. Radio clocks and GPS time receivers will ensure that the
time source is accurate to the NIST time reference. The ENF database computers
should be behind a firewall if NIST NTP-1305 is used to synchronize the
database time source, however, it is not recommended to connect the database to
the outside world through internet connections because of the security risk. Since
the ENF database computers are only used for acquiring ENF information they
should not have unnecessary software installed on them for activities such as
analysis. ENF analysis should be carried out on separate workstations from the
The offset between the primary and secondary databases should be 12
hours; one database starts to record ENF at midnight, the second one at noon for
example. This ensures that there will be no gap in data between recorded files
and to protect against loss of data if it is necessary to shut either system down.
Access to the database should be limited and only authorized personnel should be
allowed to operate the database. In addition, the database should be kept behind a
locked door to prevent any unauthorized tampering. All access to ENF
acquisition systems shall be documented and when files are copied for analysis,
SHA-1, SHA-256, SHA-512 or MD5 HASH values shall be computed for all
ENF .WAV PCM files, which is stream-lined with ENF Database Manager
software, see section 3.10.3.
When ENF databases are implemented as proposed, it is possible to further the
research of ENF applications in forensics, compare and cross verify different
ENF extraction algorithms as shown in Figure 25, and to combine them in
forensic cases by using multiple methods to analyze evidence.

Figure 23 ENF Database Acquisition System
Figure 24 Suggested ENF Database Structure

FFT ENF Analysis Averaged over 1 sec, mean=60.0053, std=0.012083
TT 60.051 ..................................(.........-
z V i
HI 59.95 -......................................................................
500 1000 1500 2000 2500 3000 3500
time [sec]
ZCR ENF Analysis Averaged over 1 sec, mean=59.9954, std=0.043752
0 500 1000 1500 2000 2500 3000 3500
time [sec]
Figure 25 Three ENF Extraction Methods
3.10 Other Areas to Pay Attention to
Given the number of variables in a forensic ENF database, the potential
number of configurations is far greater than one thesis can outline. In addition,
the advances in technology are so rapid that a proposal for a single ideal forensic
ENF database configuration that will be immune to all sources of error is not
feasible. The sections above have been detailed in a manner that will give general
background of the subject and give general recommendations for configuring a
forensic ENF database in a way that will help minimize the amount of error
while simultaneously strengthening the forensic validity of the database. There
are three other considerations that should be briefly mentioned so that the
examiner can be aware of what the future might hold for ENF.

3.10.1 Proposed Changes to ENF Thresholds
In a June 25, 2011 article found at ( details
about proposed changes to the frequency thresholds of the United States
electrical grid imply that clocks could lose time. The North American Electric
Reliability Corporation (NERC) oversees the United States electrical grid and
has proposed that the frequency thresholds be expanded to help make the grid
more reliable [65], The expansion of the thresholds would mean that plug-in
clocks, like those on microwaves, alarm-clocks, or ovens, could run up to 20-
minutes fast over the course of the next year in the US Eastern Grid. The grid-
based clocks keep their time by counting cycles. If the thresholds are expanded
then the number of cycles per second will increase and/or decrease causing the
plug-in clocks to count more or less cycles per second and in turn cause the
clocks to run fast or slow. For example, if the network frequency runs 5 mHz
high (60.005 Hz) for 10 hours, a clock will speed up by 3 seconds [(60.005 -
60.000) / 60 10 3600 s/hr = 3s],
The implications of the proposed changes mean that the methods to
monitor and extract ENF will need to be followed closely while these changes go
into effect. The expansion of grid frequency thresholds means that the thresholds
used in ENF extraction will also need to be expanded. Depending on what the
thresholds are changed to and how far the signal is allowed to vary from 60 Hz,
these changes could affect the way automated searches, spectrographic
extraction, and zero-crossing methods are applied. See reference [16], [65], or the
NERC website for more information.
3.10.2 Neutral Interference at the Signal Source
Another important consideration to pay attention to is the stability of the
power source used to power the ENF probe. As a colleague at the NCMF, Jack
LeRoi, pointed out the neutral side of the socket should, in theory, be carrying
zero volts. The positive side should be carrying the 120 volts (US grids). This
ideal situation is not always the case however, if the neutral side is carrying some
voltage it can cause unwanted affects in the signal, potentially altering important
characteristics such as zero-crossings. Some of the sockets at the NCMF were
measured and found to be carrying as much as 6 volts on the neutral side of the
socket. By comparing the socket output on an oscilloscope, the severity of these
distortions could be seen. There is a considerable amount of research going into
this phenomenon and the impact it has on ENF, but for now the amount of error
that is introduced to the signal has not been quantified.

3.10.3 ENF Database Manager
To help manage the files in a forensic ENF database the author developed
a file management program using MathWorks MATLAB. This program is
designed to help stream line the initial processing of ENF database files so that
they can be easily organized into the sub databases (see Figure 25). The program
is called ENF Database Manager and is designed so that the ENF file
origination path can be selected and the ENF file destination path can be
selected. There is also a section where text can be entered if the files should be
appended. Next, MD5, SHA-256, and/or SHA-512 HASH values can be
calculated for the original 6 kHz 8 kHz file. Down-sampling can also be
managed by selecting keep original, 144 Hz, 360 Hz, or custom. Band pass
filtering can also be managed by selecting keep original, 60 Hz, 120 Hz, or
custom. With this program the user can easily process ENF database files and
organize them efficiently, presented in Figure 26.
v ENF_Database_Manager
Select ENF Database File Origination Path
::^ENF_West_DB>PC1 \ENF_PC1 _111003.wav
Select ENF Database File Destination Path
Append File Wtth |DS144_BPF60
HASH Down-Sample Band Pass Filter
3MD5 O Keep Original O Keep Original
3 SHA-256 144 Hz 60 Hi
Sha-512 O 360 Hz O 120 Hz
Start O Custom [ 1 O Custom
Figure 26 ENF Database Manager

4. Proposal for Broadcast-Type Forensic ENF Databases
This chapter focuses on the possibility of broadcast ENF databases that
can be used to capture ENF variations in one location and broadcast them to a
remote location. The forensic need to design such a database configuration has
developed over the course of the last decade with the increase in foreign conflict.
Recently there has been an increase in the number of audio/video recordings
being used to depict graphic scenes, communicate threats or other messages, and
pass along sensitive information. Often times, such audio/video recordings will
be made in places such as a cave or bunker and the recording equipment will be
powered by generator. The recordings are often then brought to broadcast
stations and possibly used to gain public attention or raise awareness of coming
events. The ENF Criterion can produce valuable information from such
recordings such as date and time of creation, areas of potential edits, mixed
material, and geo-location. Armed with information such as this, law
enforcement and military can proactively deploy valuable resources in a more
efficient manner.
In a recent case, presented by Grigoras [16], a video tape broadcast was
submitted to have the authenticity determined. Using the ENF Criterion it was
possible to determine that there were three ENF traces in the recording. ENF1
was introduced during the digitization process, ENF2 was introduced by the
broadcast company when the recording was broadcast, and ENF3 was introduced
onto the original tape by a 220 V 50 Hz generator. Information such as this can
be instrumental in narrowing down possibilities.
4.1 Scope of Broadcast-Type ENF Databases
Certain areas around the world that may be of interest for acquiring ENF
information can have complex electric grid networks. Each network will have a
unique ENF signature and such networks can cover small or large geographic
areas. Some of these geographic areas can be places of violent opposition from
local inhabitants or intense battle. Sending a forensic examiner with no combat
training into these areas can be disastrous for innocent bystanders, soldiers, and
the forensic examiners. On the other hand, sending a soldier with no forensic
technical background into these areas can be disastrous for configuring a
complex ENF database. This section offers a simple maintenance-free solution
that will allow for electric network monitoring of any grid from a safe distance.
This solution can provide the necessary ENF information with a one-time
installation of a radio, Bluetooth, or Wi-Fi ENF probe into the grid of interest.

The installation of a broadcast ENF probe only requires that the device is
plugged into any wall socket on the grid of interest. Once installed the broadcast
ENF probe will transmit the ENF variations of that grid back to a safe base-
station such as a military base where, once received, will be recorded onto a local
acquisition computer the same way that the traditional database would.
4.2 Frequency-Modulation Databases
Radio has a long history dating back to the late 1800s and a collective
group of contributors to its invention. There is debate about who the premier
inventor of the radio is, some sources claim Guglielmo Macroni as the father of
radio but there is evidence that he used several of Nikola Teslas patents to
broadcast the first transatlantic transmission. Today, commercial radio broadcasts
can fall into several categories: Amplitude Modulation (AM radio), Frequency
Modulation (FM radio), Digital Broadcast, Satellite Radio, and the list can be
extended. AM radio utilizes changes in amplitude at a given frequency to drive
the output signal, in other words when the source signal is in compression the
amplitude increases and when the source signal is in rarefaction the amplitude
decreases. FM radio utilizes a high carrier frequency that changes phase slightly
over time to relay the differences in source signal, in other words when the
source signal is in compression the carrier frequency is slightly faster and when
the source signal is in rarefaction the carrier frequency is slightly slower. AM
and FM radio are forms of analog modulation, satellite radio and digital
broadcast are forms of digital modulation. Digital modulation is similar to the
way NIST broadcasts from the WWVB radio station in Fort Collins, Colorado
where signal strength is reduced by a certain amount at the beginning of every
second to represent a certain binary value and then depending on the amount of
time it takes for the signal strength to reach another threshold will signify another
bit value (see section 3.2). There are several methods for digital broadcast
encoding, the focus of this section is on analog type broadcasts and more
specifically on FM radio because of the superior dynamic range and resilience to
interference when compared to AM radio.
An experiment was conducted during the course of this thesis as a proof
of concept to prove that an ENF database could be recorded remotely with no
physical connection to the source providing the ENF. In configuration-1 of this
experiment, a FM transmitter was built from the Ramsey Radio Kit (FM10)
available at This FM radio kit was equipped
with RCA left and right inputs and a 9 volt power supply. To tune the radio a
simple adjustment of the coil mechanism was done by connecting an mp3 player

to the FM transmitter and then listening to an open frequency on a car stereo, the
coil mechanism was rotated until the signal from the mp3 player could be heard
clearly. Next, an ENF probe was plugged into a wall socket and the ENF probe
RCA outputs were connected to the FM transmitter RCA inputs. The FM
transmitter was plugged into a wall socket to power the device. Using an
Olympus WS-760M hand held digital recorder capable of receiving FM radio
broadcast the signal from the transmitter was received and recorded on the other
side of the building (about 30 feet). The duration of the first recording was about
1 hour and then the file was processed to investigate the applicability of using the
spectrographic extraction method. The result was a strong ENF signal around 60
Hz that clearly showed the small unique variations that could be used in a
forensic comparison. There was a substantial amount of background noise in the
transmitted file, however, the concept proves to be possible if high quality
components and a real transmitter are used. After doing some minor adjustments
of the transmitter and changing the broadcast frequency, nine hours of ENF were
broadcast and recorded onto the Olympus; this resulted in a SNR of 61 dB on the
received transmission. The database recording had a SNR of 81 dB. In Figure 27
the lower spectrogram is roughly three hours of the broadcast signal received on
the Olympus recorder down-sampled to 144 Hz and band-pass filtered around 60
Hz. The upper spectrogram is a database file from an ENF acquisition database
(TFSL, Minneapolis), processed the same.
Figure 27 FM Radio Broadcast

To ensure that the power supply was not introducing ENF into the
transmission signal and that the received signal was in fact an ENF broadcast,
other configurations were tested. By connecting the output of an mp3 player to
the transmitter and broadcasting music that was then received by the Olympus
recorder the test showed that the processed file contained no ENF. To further test
the possibility of introducing ENF from the transmission, receiving, and
recording equipment other tests were carried out at the NCMF that involved two
more configurations of an FM receiver, a recording device, a car battery, a
laptop, and a power inverter.
Configuration-2: Charge the car battery, connect the power inverter to the
battery posts, and power the FM receiver, a Sony PCM D-50, and the laptop from
the inverter. In this configuration none of the equipment was connected to the
electric grid. The output from the FM receiver was sent to the input of the Sony
and the output of the Sony was sent to the input of the laptop and then the signal
was recorded using RecAll Pro. The FM receiver was tuned to the National
Weather Service channel which broadcast 24-hours a day on 162.558 MHz. Only
5 days of ENF was recorded because the equipment would shut off when the
battery charge was drained and then the battery would need to be charged and the
system turned on again. However, the results indicated that the equipment was
not in a strong electromagnetic field because there were no usable ENF traces in
the recordings. From configuration-2 it could be said that the specific
transmission and recording equipment, in that specific location, was not
introducing ENF into the recordings.
Configuration-3: Connect the FM receiver to a wall socket, send the
output to the input of a Sony PCM D-50 that was mains powered, send the output
of the Sony to a laptop that was mains powered and record the signal using
RecAll Pro. The FM receiver was tuned to the National Weather Service channel
which broadcast 24-hours a day on 162.558 MHz. With this configuration, 7 days
and 22 hours of ENF material was recorded. After examining these recordings it
was determined that there were no usable ENF traces. This illustrates the point
that the equipment used in this test was not in the presence of a strong
electromagnetic field or susceptible to mains induced ENF bleed.
To determine if the Sony PCM D-50 was capable of capturing ENF, tests
were made with the device in various areas such as next to a computer or other
strong electromagnetic sources and then the recordings were examined and
revealed that the Sony can capture ENF even when the device is battery powered
only. From the various configurations above it can be said that ENF from

Configuration-1 was introduced by the broadcast probe and not by other means
and that there is validity to a FM broadcast ENF database. With some minor
adjustments and decent equipment a strong and clean broadcast signal can be
obtained and the SNR can be increased beyond 61 dB. These devices can be
small in dimension and have an ENF probe circuit installed. The electric network
voltage can come from a wall socket and upon entering the device be split off
into two branches, the first branch going to the transformer to power the
transmitter itself and the second branch going to the transformer to step the
voltage down to 6VAC for the ENF probe circuit. The output of the ENF probe
circuit can be hard-wired to the input of the transmitter and when the transmitter
is plugged in it will continuously transmit ENF.
There are some disadvantages to establishing an FM broadcast ENF
database such as: transmission interruptions, loss of signal, radio jamming from
sophisticated attacks, and the list can be extended. FM signals can be intercepted
relatively easily, although if an ENF signal were to be intercepted the recipient
would probably dismiss it as a communication channel with severe ground
noise. From a security perspective analog transmission cannot be encrypted but
there are other precautions that can be used such as continuous frequency
modulation techniques that constantly change the carrier frequency at variable
speeds, making the signal difficult to follow for any extended period of time. But
the potential for an ENF database to exist separately from its source, receiving
information wirelessly, does exist and the concept has been proven using FM
radio broadcast.
4.3 Blue-Tooth Databases
Blue-Tooth is a wireless communication technology that is mostly used in
phones, cars, and medical devices to transfer information like audio, sensor data,
and health statistics. Blue-Tooth is designed to operate on the 2.4 GHz radio
band and consume a small amount of power which makes it useful for mobile
and localized applications. Blue-Tooth can be implemented, with distance
limitations, in an ENF probe and used to transfer information about the electric
grid variations wirelessly to a remote location that is within roughly 10 meters, or
in a scatter-net configuration, within roughly 10 meters of a hop point [35].
The simple explanation for Blue-Tooth is that information coming from the
device is converted into 1 s and 0s and this data stream is modulated to the 2.4
GHz radio signal and then broadcast. On the receiving end of the
communication, the 2.4 GHz radio signal is de-modulated and the ls and 0s are
revealed in a remote location.

The world record for longest Blue-Tooth file transfer is about 1
kilometer, obtained by using sophisticated high-gain antennas [66]. With that
being said, there are obvious distance limitations to the implementation of a
Blue-Tooth ENF database. The remote location receiving the Blue-Tooth
broadcast is typically required to be within 10 meters of the source which can be
a disadvantage that perhaps FM or Wi-Fi could alleviate. The advantage to Blue-
Tooth is that the technology is relatively more secure than FM or Wi-Fi. Blue-
Tooth ENF probes could be useful for close range wireless ENF monitoring, but
if the demands are long range, then FM is superior. If the application of a Blue-
Tooth ENF probe is intended to be covert then this type of configuration could
work well in not raising suspicion with wires running from a black box into a
The reason that Blue-Tooth is more secure than FM or Wi-Fi is that
Blue-Tooth is a frequency-hopping spread-spectrum (FHSS) technology. FHSS,
as it is used with Blue-Tooth means that Blue-Tooth communications are
constantly hopping frequency channels, making it difficult to follow a single
communication stream and/or gain much information from one channel. Blue-
Tooth is a radio technology that operates on the 2.4 GHz radio spectrum and is
subject to FCC regulations. The FCC dictates that Blue-Tooth is allowed to
operate on 79 channels in the 2.4 GHz range; in addition, Blue-Tooth
communications have to be used in a pseudo-random manner across at least 75 of
the 79 channels. A Blue-Tooth device must not occupy the same channel for
more than 0.4 seconds in a 30 second window. The nominal bandwidth for each
channel is 1 MHz and the maximum peak power output allowed is 1 Watt, hence
the low power consumption but also the restricted transmission distance. Most
Blue-Tooth devices hop pseudo-randomly across all 79 channels 1,600 times per
second [35], Encryption can also be applied to Blue-Tooth communications
adding an extra security precaution that will randomize the data so that if it is
intercepted it cannot be read. Encryption is a security precaution that FM
transmission lacks because they are analog broadcasts.
If the demands for an ENF database are to supply information on a secure
wireless transmission at short distances then Blue-Tooth provides a practical
solution. An ENF probe can be configured with a Blue-Tooth transmitter in a
small and inconspicuous device that requires only to be plugged into an outlet
and then the device will need no maintenance.

4.4 Wi-Fi Databases
Wi-Fi (IEEE 802.11) wireless communications are also a possible
solution for transmitting electric grid variations from one location to a remote
ENF database. The average signal distance of traditional Wi-Fi is about double
Blue-Tooth (~65 feet). Wi-Fi works in a similar manner to Blue-Tooth with
regards to the fact that information is modulated so that it can be broadcast and
then the radio signal is received and de-modulated, thus revealing the data stream
in a remote location without the use of wires. Because Wi-Fi is a powerful
wireless tool there is a lot of demand for the technology. But Wi-Fi has broadcast
limitations just like any radio communication. For this reason there have been
technological advances to increase the distance Wi-Fi can broadcast, with one
potential use being to serve free Wi-Fi service to a geographic area the size of a
Wi-Fi broadcast distance can be increased by modifying the transmission
power, the antenna, and line-of-sight locations. The increase in transmission
power for example, will result in larger and more cumbersome devices. As
technology advances there may be practical solutions in the near future that
balance the need for small real-estate and long distance broadcast.
A Wi-Fi ENF probe could be configured to receive the electric grid
variations from any wall socket and convert the values to binary ls and 0s and
then the data stream could be modulated and broadcast via Wi-Fi to a remote
location. One work-around to the distance problem would be to broadcast the
ENF from the probe to a near-by computer with internet connectivity. Once the
signal is received into the near-by computer the ENF files can be sent via internet
to any place in the world. Once received or downloaded from the internet the
ENF data can be compiled in the database. Another solution would be to create
an ENF probe that simply outputs the data stream via Ethernet cable to the
Virginia Tech has completed some testing with wireless ENF [70] but
Virginia Tech is interested in ENF for reasons other than forensics. Virginia Tech
has developed devices called Frequency Disturbance Recorders (FDR) for
monitoring electric grid frequency variations throughout North America. The
information gathered from these FDRs is transmitted through the internet and
then compiled in a database for later reference. The FDR units are like the
NCMF ENF probe on steroids, FDRs have a built in Low Pass Filter (LPF),
Analog to Digital (A/D) converter, GPS time receiver, micro-processor, and