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The use of subjective volume adjustment as a behavioral elicitation method for audio quality listening tests

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Title:
The use of subjective volume adjustment as a behavioral elicitation method for audio quality listening tests
Creator:
Jostad, Drew Beecher ( author )
Place of Publication:
Denver, Colo.
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University of Colorado Denver
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English
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Master's ( Master of science)
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University of Colorado Denver
Degree Divisions:
Department of Music and Entertainment Industry Studies, CU Denver
Degree Disciplines:
Recording arts

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Subjects / Keywords:
Sound -- Recording and reproducing ( lcsh )
MP3 (Audio coding standard) ( lcsh )
Music and technology ( lcsh )
MP3 (Audio coding standard) ( fast )
Music and technology ( fast )
Sound -- Recording and reproducing ( fast )
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bibliography ( marcgt )
theses ( marcgt )
non-fiction ( marcgt )

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Review:
This paper argues for the utility of behavioral elicitation methods to measure listeners' aesthetic reactions to music in audio quality listening tests, and evaluates the subjective volume adjustment method for this application. Subjects' preferred listening levels were measured and analyzed with regard to enjoyment ratings and a variety of audio characteristics. A significant correlation was found between preferred listening level and enjoyment ratings, but not between listening level and audio quality. A Multiple Stimulus with Hidden References and Anchors test was also conducted on the same listening material and was found to be a more reliable indicator of audio quality.
Bibliography:
Includes bibliographical references.
System Details:
System requirements: Adobe Reader.
Statement of Responsibility:
by Drew Beecher Jostad.

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University of Colorado Denver Collections
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Auraria Library
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All applicable rights reserved by the source institution and holding location.
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987243805 ( OCLC )
ocn987243805
Classification:
LD1193.A70 2016m J67 ( lcc )

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Full Text
THE USE OF SUBJECTIVE VOLUME ADJUSTMENT AS A BEHAVIORAL ELICITATION
METHOD FOR AUDIO QUALITY LISTENING TESTS
by
DREW BEECHER JOSTAD B.A., Case Western Reserve University, 2009
A thesis submitted to the Faculty of the Graduate School of the University of Colorado in partial fulfillment of the requirements for the degree of Master of Science Recording Arts
2016


This thesis for the Master of Science degree by Drew Beecher Jostad has been approved for the Recording Arts program
by
Leslie Gaston Bird, Chair Jeff Merkel Barbara Walker
Date: December 17, 2016


Jostad, Drew Beecher (MS, Recording Arts)
The Use of Subjective Volume Adjustment as a Behavioral Elicitation Method for Audio Quality Listening Tests
Thesis directed by Associate Professor Leslie Gaston Bird
ABSTRACT
This paper argues for the utility of behavioral elicitation methods to measure listeners aesthetic reactions to music in audio quality listening tests, and evaluates the subjective volume adjustment method for this application. Subjects preferred listening levels were measured and analyzed with regard to enjoyment ratings and a variety of audio characteristics. A significant correlation was found between preferred listening level and enjoyment ratings, but not between listening level and audio quality. A Multiple Stimulus with Hidden References and Anchors test was also conducted on the same listening material and was found to be a more reliable indicator of audio quality.
The form and content of this abstract are approved. I recommend its publication.
Approved: Leslie Gaston Bird
m


ACKNOWLEDGEMENTS
Professor Leslie Gaston Bird, M.S., for inspiring me to conduct my own listening study
Eason Jostad, M.S., for consultation on statistical analysis
Kristina Ruff, for comments on earlier drafts
Dinsmore Tuttle, for guidance during the writing process
All the subjects who took part in the listening test, for their time and their ears
This research was conducted under review by the Colorado Multiple Institutional Review Board, Protocol Number 16-0338
IV


TABLE OF CONTENTS
CHAPTER
I. INTRODUCTION.........................................................1
Disputes Concerning Perceptual Coding................................1
Audio Quality Listening Tests........................................4
Music Enjoyment Tests: Elicitation and Confounding Variables.........7
Studies of Music Enjoyment and Audio Quality........................13
Focus of the Present Research.......................................17
II. FIRST PHASE OF STUDY...............................................20
Methods.............................................................20
Results.............................................................28
Discussion..........................................................34
III. SECOND PHASE OF STUDY.............................................37
Methods.............................................................37
Results.............................................................41
Volume Adjustment Test..............................................41
MUSHRATest..........................................................48
Discussion..........................................................54
REFERENCES................................................................57
APPENDIX
A. Rating Sheet Used For Both Phases of Study.........................61
B. Distribution of Test Conditions for MUSHRA Test....................62
v


C. Raw Data..............................................................63
Phase One............................................................63
Uncorrected.......................................................63
Corrected to Deviations from Means................................64
Phase Two............................................................66
Volumes Uncorrected...............................................66
Level Differences with the Reference Recording....................71
MUSHRAtest........................................................74
vi


LIST OF TABLES
TABLE
2-1: Recordings chosen for first phase of study.................................22
2-2: Long-term loudness, applied gain, loudness range, and spectral centroids for
phase one test items.........................................................25
2-3: Aggregated results from phase one of study. Values used are corrected to
deviations from each subject's mean..........................................29
2-4: Aggregated results for phase one. Listening level values are corrected to
deviations from each subject's mean. Enjoyment ratings are deviations from means that are then divided by each subject's standard deviation......29
2-5: Per-item correlations between listening level and enjoyment rating for phase one. Values used are uncorrected......................................................30
2-6: Per-item correlations between listening level and enjoyment ratings for phase one. Values used are corrected to deviations from each subject's mean.....31
2-7: The five items with the most significant correlations according to the average across raw and corrected data.............................................31
2-8: Correlations between the initial mastered level of a track and the significance of the correlation between listening level and enjoyment rating..............35
2-9: Correlations between the mastered level of a track and the amount of variance in listening level explained by variance in enjoyment rating.................33
2-10: Correlations between the spectral centroid of a track and the significance of the correlation between listening level and enjoyment rating..................33
2- 11: Correlations between the spectral centroid of a track and the amount of
variance in listening level explained by variance in enjoyment rating.......33
3- 1: Recordings used in second phase of study.................................38
3-2: MP3 coder bit-rates used for test items in second phase....................38
3-3: Test conditions for Volume Session One in Phase Two........................38
3-4: Aggregated correlations between listening level and enjoyment ratings, both measured as deviations from a subject's mean. Includes data from phases one and two...................................................................42
vii


3-5: Correlations between level and enjoyment, measured as deviations from each subject's mean, for the four recordings used in both phases of the study. Data is aggregated across both phases...............................................42
3-6: Averaged difference values for phase two of the study, as well as standard
deviations and p-values.....................................................42
3-7: ANOVA results for volume adjustment test..................................45
3-8: Correlation between listening order and LUFS measurements of listening level.47
3-9: Data from the two listening subjects whose listening levels showed significant correlations between item order and listening level.........................47
3-10: Statistical analysis of subjects grouped by the sign (+/-) of the correlation
between item order and listening level......................................47
3-11: Distribution of subjects according to test conditions and listening order
correlation groups..........................................................47
3-12: Means and variances for MUSHRA audio quality ratings of Item MA........50
3-13: Means and variances for MUSHRA audio quality ratings of Item MB........51
3-14: Means and variances for MUSHRA audio quality ratings of Item MC........52
3-15: One-way ANOVA results for MUSHRA data.....................................53
3-16: Pairwise comparisons with reference recording for all MUSHRA items.......53
viii


LIST OF FIGURES
FIGURE
1-1: Sample of MUSHRA test interface [17]........................................5
1- 2: Quality scale for ABC test [1]............................................5
2- 1: The Max patcher for measuring spectral centroid..........................24
2- 2: Example of the automation recorded for the first phase of the study......27
3- 1: Example of the automation recorded in Pro Tools for phase two............40
3-2: Averaged listening levels (LUFS) for item MA, corrected with regard to reference recording..................................................................44
3-3: Averaged listening levels (LUFS) for item MB, corrected with regard to reference recording..................................................................44
3-4: Averaged listening levels (LUFS) for item MC, corrected with regard to reference recording..................................................................45
3-5: MUSHRA audio quality ratings for Item MA...................................50
3-6: MUSHRA audio quality ratings for Item MB...................................51
3-7: MUSHRA audio quality ratings for Item MC...................................52
IX


LIST OF ABBREVIATIONS
ABC: A listening test methodology also known as double-blind triple-stimulus with hidden reference
ANOVA: Analysis of Variance
dB (A): Decibels of Sound Pressure Level, A-weighted
EBU: European Broadcasting Union
EBU-R128: European Broadcasting Union Technical Recommendation 128
EEG: Electroencephalogram
fMRI: Functional Magnetic Resonance Imaging
GUI: Graphical User Interface
Hz: Hertz
kbps: Kilobits per second
ITU: International Telecommunications Union
ITU-R Bs.: International Telecommunications Union Recommendation for Broadcast
LU: Loudness Units
LUFS: Loudness Units relative to Full Scale
MP3: Motion Picture Experts Group Layer III
MUSHRA: Multiple Stimulus with Hidden Reference and Anchors
PET: Positron Emission Tomography
SPL: Sound Pressure Level
x


CHAPTER I
INTRODUCTION
Disputes Concerning Perceptual Coding
Unlike traditional retailers of physical music media, such as compact discs, online music sellers and streaming services have to decide for themselves what quality of music they will offer consumers. Most online stores do not offer the absolute best quality available, because that would result in longer download times, more buffering for streams, and higher costs for server space. The technology that has allowed delivery of adequate-quality music while minimizing such costs is called perceptual coding, whereby the music data is digitally coded with regard to not only the recorded content but also to the perceptual capacity of the intended audience. In short, if a listener will not notice something, there is no reason to waste data on it. The implementation of this idea has played a major part in making music delivery via the Internet possible.
Although the MP3 file is probably the best-known version of this idea with regard to music and sound, it is by no means the only or even the first one. During the development of telephone systems, Bell Labs engineers realized that speech does not have to be produced in its full frequency range to be intelligible. So, to lower costs, they focused on reproducing only the important frequency range, which is around 4000 Hz wide. Similarly, the team behind the 1980 Red Book standard for CDs decided that a maximum frequency range of 22050 Hz and dynamic range of 96 dB would, due to the limits of human hearing, be enough to accurately represent any recorded music. Increasing those limits would have made CDs hold less material and cost more to produce.
1


Perceptual coding is an application of this same philosophy: you can save data by
(1) not bothering to code music elements that are inaudible anyway, and (2) hiding added noise where it cannot be heard. This is made possible by a psychoacoustic phenomenon called frequency masking, where a listeners brain fails to perceive a sound due to its proximity, in terms of frequency, to a simultaneous sound of greater amplitude. Psychoacoustic masking models are used by perceptual encoding systems to allocate data to the frequency bands where it is most needed, while saving bits where possible. In order to test these technologies, subjective listening tests were conducted on the resulting audio. After all, the best way to check if coding noise is inaudible is to play the recording for expert listeners and ask them if they can hear it.
Subjective listening tests, at their most basic level, amount to playing two versions of the same recording and asking which one sounds better. A test with exactly that design would be unscientific, but that is the central idea. The specific standards for listening tests are published by the International Telecommunications Union (ITU) [1],
[2] and European Broadcasting Union (EBU) [3]. These documents lay out specific guidelines for these tests, including rules for room acoustics, test interfaces, and statistical analysis. Throughout the 1990s and 2000s, tests were conducted, usually according to these standards, to attempt to establish a quality benchmark for the delivery of perceptually coded audio [4][12]. In [6], one coding/decoding scheme, or codec, delivered adequate results at a data rate of 128 kilobits per second (kbps). This bit-rate was, for a time, the standard used by the iTunes music store. A later test [12] shows that another popular codec achieved transparency at bit-rates of at least 256
2


kbps, and several online services, including iTunes, Amazon, and Spotify1, now deliver their music at or above this quality level.
In theory, then, the bit-rates of online music have kept pace with the research to determine the necessary quality levels. However, perceptual coding technology still has its detractors. In the movie The Distortion of Sound, some musicians and audio engineers maintain that perceptual coding, no matter the data rate, seriously diminishes the emotional power of music [13]. This view is by no means widely accepted, and the arguments made in the film have been challenged by [14], [15]. This paper will not take a side in this debate, except to say that more research is needed on the topic. Because perceptual coding is the dominant format for music delivery in the digital age, the suggestion that it presents an emotional handicap to recorded music deserves to be taken seriously. Audio engineers, as the intermediaries between the musical and technological communities, are responsible for ensuring that technology is delivering the music in a way that is true to the artists expression. But this is a charged issue that demands calm, methodical science not alarmist ]umping-to-conclusions.
The thrust of the argument made in both The Distortion of Sound and mixing
engineer Andrew Scheps lecture "Lost in Translation [16] seems to be that the
aforementioned listening tests that have rubber-stamped our current standards for
perceptual coding do not tell the whole story. In his talk, Scheps wonders if differences
in audio quality, no matter how slight, may affect our aesthetic response to the music.
He hypothesizes that even though our brains may be capable of filling in the "blanks
left by perceptual coding, that extra (unconscious] cognitive effort can diminish the
1 Spotify offers 320 kbps streaming to paid users only; free users receive 128 kbps streaming.
3


emotional experience of the music. "If you love an artist, he says, "it doesnt really matter. You will love them even if it sounds terrible... But what if youre sort of on the fence? What if the audio quality actually determines ... whether youre going to listen to the next song on that record? [16]. This idea will be referred to as the Scheps hypothesis: perceptual coding can affect the aesthetic appreciation of music, potentially in unconscious ways.
My research will attempt to test this hypothesis. But the first step is to pursue a research methodology that can address the emotional or aesthetic reactions people have to music, instead of just their ability to notice coding artifacts and compare recordings. This turns out to be a complex question that has received little attention in audio engineering circles.
Audio Quality Listening Tests
In the subjective listening tests cited earlier [4][12], the goal was to determine quality thresholds for conscious discrimination between CD-quality audio and that same audio encoded using a perceptual algorithm. These tests tend to use one of two study designs: MUSHRA, detailed in [2], and ABC, detailed in [1]. The MUSHRA test, or Multiple Stimulus with Hidden Reference and Anchors, is applied for moderate or large impairments, when the experimenters believe their subjects will be able to identify the full-resolution sample with reasonable consistency. In this test, listening subjects attempt to discriminate between up to nine different versions of one sample, as well as to quantify the differences between the algorithms or bit rates on a quality scale. Figure 1-1 shows the interface used for such a test. Letter R represents the full-quality
4


Please rate the Basic Audio Quality of the following excerpts using the scales provided...
65 100 88 35 18
Figure 1-1: Sample of MUSHRA test interface [17].
100
80
60
40
20
0
Impairment Grade
Imperceptible 5.0
Perceptible, but not annoying 4.0
Slightly annoying 3.0
Annoying 2.0
Very annoying 1.0
Figure 1-2: Quality scale for ABC test [1].
5


reference, and Letters A-F represent the items under test, one of which is a hidden duplicate of item R. This methodology provides a good balance between subject focus and efficient use of time; since the impairments are moderate to large, it is reasonable and efficient to have a subject compare several recordings at the same time.
If the impairments under test are very slight, and the subjects are expected to have a harder time identifying which sample is CD-quality, the recommended test is called ABC, or double-blind triple-stimulus with hidden reference, and is detailed in ITU-R Bs. 1116 [1]. In this test, subjects hear the reference recording, termed the A recording, and then two other items. One of these items is the reference, and one is the perceptually coded version. The subject is asked to identify which version is not full quality, and estimate the amount of distraction/annoyance added by any audible coding noise. Figure 1-2 shows the quality scale recommended for this test. The scale is intended to have a resolution of 0.1. This test is used for small impairments because it gives the subjects more time to listen for minute differences, and only asks that they focus on one comparison at a time.
These tests are clearly aimed at conscious discrimination between audio excerpts and critical focus on audio quality; careful study in this area has been instrumental in the expansion of digital music delivery over the Internet. But such tests cannot address Andrew Scheps hypothesis: that the audio quality of a recording might affect a listeners aesthetic appraisal of the recorded music, even subconsciously. This aesthetic judgment is not a comparison with a reference recording, nor is it a quantitative appraisal; as such, it is distinct from the response measured using the MUSHRA and ABC methodologies. In order to test the Scheps hypothesis, we need a
6


study where subjects experience the recordings as if they were listening to music for pleasure, not listening critically to audio minutiae. There are several challenges to this research focus:
1. How can the pleasure response to music be scientifically measured? That is, what is the most effective method of elicitation?
2. How can we isolate the effect audio quality has on this response, to the exclusion of confounding factors such as interpersonal variety in music preference, listener familiarity with test recordings, and even day-to-day variation in a listening subjects mood?
In the next section, I will review the literature that is relevant to these questions.
Music Enjoyment Tests: Elicitation and Confounding Variables
The different elicitation methods I will discuss are: direct questioning, behavioral elicitation, and physiological measurements. Direct verbal questioning is the most straightforward method, and the most commonly employed. Subjects are simply asked to rate a song on a particular scale according to their enjoyment. North and Hargreaves, in [18], use a scale from zero to ten, but other scales can be used as well. The main advantage to this method is its simplicity: it is easy to implement and record, and almost all listening subjects will find it easy to understand. Further, it has been used so often that a new researcher can easily find how to use these data for statistical analysis. But this method has certain shortcomings that are not always acknowledged.
It trusts that the listening subjects understand their aesthetic response accurately enough to estimate it quantitatively, which may not be the case. As Daniel Berlyne puts it:
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"Although we know that verbal expressions of preference must reveal a great deal about nonverbal forms of behavior, we rarely know exactly what. And before we can know, much more research will have to be devoted to the specific question of how verbal and nonverbal responses to art are related [19].
Samantha Warren has discussed this same issue in relation to workplace aesthetics
[20]. She points out that after an aesthetic experience, the subject turns inwards to
judge or interpret what has just happened. And these judgments/interpretations are
shaped by the subjects vocabulary and cultural context, with the result that the initial
aesthetic experience can be warped merely by ones effort to understand and
communicate it.
Some of the difficulties of direct questioning have also been addressed with regard to audio listening tests. Precoda, Meng, and Kreiman [21] argue that the task of estimating the magnitude of a quality degradation is more challenging than it is assumed to be, and that it is unlikely to be comparable across subjects, or even replicable by one subject on different days. Sporer [22] gives statistical support to this position: in a study that compared results from multiple ABC listening tests, expert listeners could not replicate their ratings when presented with the same test items some months later. This suggests that the scale of degradation recommended in ITU-R Bs. 1116 (Figure 1-2) is not as absolute as it was intended to be. Lastly, in the area of spatial fidelity, Mason et al. [23] give an extensive summary of the challenges presented by using verbal elicitation techniques for subjective tests. While not concerned with perceptual coding, the authors of [23] show that, even in the case of a strict audio quality test, nonverbal elicitation can reveal things that direct verbal questioning cannot. It seems natural that this could also extend to investigations of music enjoyment, given the arguments of Berlyne [19] and Warren [20] cited above.
8


Altogether, it seems clear that while direct questioning is the easiest method, and has been successful in several studies, it is not without problems.
Semantic differential, as employed in [24], is a less direct method, but one that still involves using words to gauge an aesthetic reaction. It is also less common than direct questioning, so a study that employs it for audio quality would be more exploratory in nature. In this elicitation method, subjects are given a series of scales, with opposed adjectives at either end, and asked to choose a location along the scale that best fits the audio stimulus. When measuring enjoyment, adjectives with pleasant connotations (soft, sweet, etc.) are presented alongside those with unpleasant ones (hard, bitter). If subjects tend to choose more pleasant adjectives, it is assumed they had a more pleasant aesthetic experience. In this situation, the experimenters would have the added task of generating a list of adjective pairs that is appropriate for their music selection. As different adjectives are more suited to different styles of music for heavy metal, for example, the word brutal denotes a good thing this method may require that the test confine itself to either a single recording, or at least a single genre. This technique is rarely used in audio quality tests, with the exception of [24], but [25], [26] demonstrate how it can be used to measure diverse dimensions of the emotional response to music. Statistical analysis of these results is also more complex than that seen with direct verbal ratings.
When a listening study measures subjects reactions rather than verbal responses, it is using what is called a behavioral elicitation method. In some such studies, subjects are asked to react to the musical stimulus by controlling either the volume [27] or listening time [28]. The experimenters closely measure these
9


adjustments and assume that people tend to listen to more pleasurable music at a higher volume and for a longer period of time. This method elegantly removes the experimenter from part of the test, allowing the subjects to make their own decisions about the music without having to directly report what they like or dislike. A problem here is that these methods are also relatively rare, and their core assumptions that higher volumes and longer periods of listening necessarily mean higher enjoyment levels are more presumed than they are proven. The only way to prove these assumptions is to compare the results from a behavioral elicitation method with those from another method listed here, which would mean trusting the accuracy of that second method. For example, [29] did show a positive correlation between listening volumes and enjoyment ratings, which implies that direct questioning and volume adjustment are measuring somewhat the same things. The question remains whether either one is accurately measuring the aesthetic response to the recording. Another thing to keep in mind is that behavioral elicitation has the potential for added noise in the measurement: what if someone turns up a song they dislike, just because they are having difficulty hearing a certain instrument? Such a situation could easily confound a test based on volume adjustment, particularly if the effect on aesthetic enjoyment to be measured is a subtle one.
The last elicitation category is physiological measurements. Brain imaging has been used to measure pleasurable response to music using PET scans [30], EEGs [24], and fMRI measurements [31]. All of these correlate certain musical stimuli with increased response in reward centers of the brain. Brain imaging is perhaps the most direct measurement of a subjects experience, but it must rely upon research that links
10


certain brain responses to aesthetic pleasure or displeasure. Also, a listening test using this elicitation technique would be much more complex and expensive than one using any of the other methods mentioned above. Audio engineers would need to find experts in psychology or neuroscience departments to advise them in the use of these specialized techniques.
Brain imaging is not the only physiological elicitation method. Responses in other parts of the body have also been tested, particularly in research on intensely pleasurable or chilling responses to music. However, different studies are somewhat conflicted on what measurements best correlate with these intense responses to music. The authors of [30] found correlations with heart rate, respiration, and muscle response, but did not measure a significant change in electrodermal activity. In contrast, electrodermal activity is the most significant variable in several more recent studies such as [32]-[35]. Despite this conflict, these physiological tests represent additional tools that an investigator could use to measure the relationship between audio quality and aesthetic response. Using a finger electrode assembly would be simpler than having to gain access to, for example, an fMRI machine. Still, due to the specialization necessary to effectively use these data, any audio engineer hoping to measure these factors should consult experienced researchers in psychology. It also must be remembered that the research here cited is specifically concerned with chills, which are a somewhat rare experience of aesthetic ecstasy. So measuring a response such as electrodermal activity is not guaranteed to produce results, as subjects are by no means guaranteed to experience chills when listening to any given song.
11


In addition to having an appropriate elicitation method, a study aimed at the effect of audio quality on music enjoyment must control for extraneous factors that influence music listening, of which there are many. Much of the research in this area focuses on specific musical features, such as tonality and rhythm [33], [36], [37]; these factors are unlikely to confound audio research because different pieces of music are kept statistically isolated. The factors that are pertinent to audio research on the listening experience are familiarity and inter-subject preference.
Research has shown that familiarity correlates with higher subjective enjoyment ratings [38] and increased brain activation [31]; even among songs that are disliked, those that are familiar are given higher ratings than those that are not [31]. In addition, subjects provide higher ratings for music that sounds more "common or "typical, even if they have never heard the recording under test [18]. Going back to the Scheps hypothesis, he appears to address ones initial aesthetic judgment of a recording, which occurs within the first few hearings. As such, and given the studies cited, any study testing this hypothesis must recognize the influence of familiarity. Popular or classic music should not be chosen as listening material, the data from any subject who recognizes a test item should be excluded. In addition, any one song should not be repeated too many times in one session, as any subtle influence of varying audio qualities could be overcome by the subjects increasing familiarity with the music. It is easy to imagine that once subjects recognize a song, their previous memory of it colors their response significantly. Experimentally, this phenomenon is less straightforward: Verveer et al. [39] and Hargreaves [40] show that the effect of repetition depends
12


greatly on the optimum level of familiarity for each particular listener, and its interaction with the level of complexity of the music.
Another factor to control for is simple variation in music preference among subjects. Unless the study confines its analysis to within-subject differences, there must be a way to normalize enjoyment ratings across subjects to counteract the variation in genre preference. While there have been interesting studies into the roots of variation in music preference [41]-[45], it remains a daunting task to find a way to control for genre preference variation using some type of questionnaire that takes into account personality as well as formal music training [46]. Musical appetites can also vary within any one subject, whether due to momentary arousal level [28] or day-to-day mood [47]. Researchers attempt to control for these factors in different ways: choosing music from one relatively homogeneous genre [18], [38]; presenting subjects with only one song throughout the entire test [24]; or using music that was specifically written for use in the listening test [28]. As long as researchers are aware of this factor, statistical controls can be built in to any study. Before arriving at the details of the present research, I will summarize the research by two teams that have pioneered the study of the relationship between audio quality and aesthetic experience: Schoeffler and Herre, and Oohashi etal.
Studies of Music Enjoyment and Audio Quality
While not specifically concerned with distortion from perceptual coding, research by Schoeffler and Herre has investigated the relationship between audio quality and listener enjoyment. They have been one of the first teams to attempt to elucidate what effect audio quality has on what they call the "Overall Listening
13


Experience. In [48], [49], it was shown that listener enjoyment of music goes up when moving from a mono mix to a stereo mix, and again from stereo to 5.1 surround. Other work [50], [51] has demonstrated the same result from increasing the bandwidth of a recording, from low-pass filtered content up to full 20 kHz frequency range. Two attempts to correlate perceptual encoder bit-rates with listener ratings did not return significant results [51], [52].
With the exception of [52], a data-mining study using YouTube video ratings, Schoeffler and Herres methodology is as follows. First, subjects rate their enjoyment of a reference version of a song without specifically concerning themselves with audio quality, using the open-ended question "How do you like this song?, which is answered on a five-star scale. Then they repeat this rating using a version of the same recording that has undergone quality degradation, either through down-mixing, distortion, or bandwidth limiting. The difference between the two ratings is taken as the effect of the quality degradation on the subjects enjoyment of the song. This difference was shown to be statistically significant for down-mixing and bandwidth limiting.
In [50], the test material was delivered to subjects online, and they performed the task in their home listening environments. This is an interesting change from most listening tests, where the listening environment is controlled across subjects. The authors trusted their subjects, who were all considered expert listeners, to listen on professional-grade equipment in a quiet listening space. Further, it could be argued that their subjects were more likely to have an authentic aesthetic experience since they were allowed to listen in a familiar, comfortable space. Music enjoyment can be a
14


fragile thing, and expecting someone to experience music in the same way in a controlled, laboratoiy-like setting could be problematic.
In order to minimize the effect of familiarity with the recordings, the online test interface required subjects to wait a minimum of five hours in between listening sessions. The authors chose this length of time according to research on auditory memory that shows it to be past the limit of long-term auditory storage. With regard to inter-subject variation in music preference, these studies correct the absolute ratings to differences that can be compared across subjects: the key variable is the difference in rating between the reference, or high-quality, version of an item and the degraded version. So someone whose rating went from a 5 to a 4 registers the same number as someone whose rating went from a 2 to a 1. Most of these simple correction techniques will be applied similarly in the research project reported here.
The research team lead of Oohashi, Nishina, Honda, Yagi, etal. has also published interesting studies in this area [24], [27], [53]-[55], while taking an approach that contrasts greatly with that of Schoeffler and Herre. The focus of Oohashi et al. is the effect of content above 22 kHz on music enjoyment. While this is a controversial and complex area that is outside the focus of the current project, a review of their methodology will be instructive, as it is more concerned with the subconscious dimension of music listening. Instead of direct questioning, this team has used EEG brain scanning, semantic differential, and subjective volume adjustment to arrive at its results. These indirect methods are necessary given the research focus: human hearing extends only to 20 kHz, so a direct comparison of recordings with and without ultra high-frequency content (using for example an ABC format] would most likely result in
15


no significant result; in fact, this is exactly what was seen in [56]. If information above 22 kHz does have an effect on the listening experience, it would have to be in a subconscious fashion, and only measurable indirectly. This is indeed what Oohashi etal. have shown: they measure significant differences in brain activity, semantic differential, and listening volume that suggest a more pleasurable experience for a listener presented with music that contains these "hypersonic frequencies [53].
In these studies, the musical content, a 60-second recording of Indonesian gamelan, was held the same across all sessions. For their physiological measurements, the authors used EEG scans because of the low noise emitted by the sensing equipment. These images show significantly greater activation in several areas of the brain when listening to the full-range sound, as compared to either high-cut sound (content below 22kHz, which is the bandwidth of a normal CD], or the low-cut sound (content above 22kHz, which sounds like silence due to the limits of our hearing]. Interestingly, these effects disappeared when the same study was repeated using headphones instead of loudspeakers [55]. The semantic differential exercise was performed as described earlier, using a list of opposed Japanese adjectives. Here as well, subjects were slightly more likely to choose pleasant words to describe the audio that included the full frequency range of the recording, including the very high-frequency content.
For their behavioral elicitation method, the authors used a subjective volume adjustment test. First, they presented the material twice at a level of 78 dB LAeq. Then, the subjects were given three repetitions of the test recording to adjust the volume using a rotary knob. Their chosen level was recorded at the end of the third hearing, and the piece was played through once more at that level. This entire process was
16


performed six times per subject: three times each for the full-range and high-cut sound. It is unclear from [27] if the subjects were given a break or if the test was uninterrupted. If the latter, that would be 36 minutes of pure listening: one minute per hearing, 6 hearings per session, 6 sessions per subject.
While this method involves many hearings of the same piece, it succeeds in measuring as precise a level as possible: several repetitions allow the subjects to make minute adjustments, and they are invested in the accuracy of their adjustments in anticipation of the final hearing, when they will have to listen to the piece without being able to touch the volume. In order to combat any influence of increasing familiarity, the order of the two different items was randomized across all trials. The measured volume difference was slight, but statistically significant.
Focus of the Present Research
Given the result from Oohashi et al. on an audio comparison that has been experimentally shown to be consciously indistinguishable [56], the utility of an indirect test methodology is an area that deserves further research. Despite the fact that Schoeffler and Herres direct questioning methodology did not return significant results for low bit-rate distortion [51], it is possible that the five-star rating scale does not have adequate resolution to measure any difference that would arise from the subtle impairments introduced by the most common codecs. Therefore, this project will focus on one indirect test methodology to see if it can provide the precision necessary for such research.
After reviewing the methods used by Oohashi et al., this paper will focus on the utility of the subjective volume adjustment test for perceptual coding listening tests.
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The scope of this project in terms of time, prior expertise, and available resources ruled out the use of a physiological elicitation method, such as EEG or electrodermal activity. It was also determined that semantic differential requires more expertise in the human sciences, and that subjective volume adjustment is therefore the most appropriate for pure audio research. Finally, the volume adjustment test is a relatively uncommon method; it deserves focus, but also needs vetting. Other than [29], there has been little research on the precise relationship between listening level and subjective enjoyment of music, or the relationship between listening level and audio quality. Hopefully this project can contribute to this discussion.
For these reasons, this project will test subjective volume adjustment to see if it can be profitably applied to audio quality listening tests. The following research questions will be addressed:
1. To what extent does a subjective volume adjustment test reflect listener enjoyment?
2. Are certain types of recordings better for this type of test than others? If so, why?
3. Do differences in coding bit rates result in significantly different subjectively determined listening levels?
4. How do the results from such a volume adjustment test compare with a MUSHRA test focused precisely on audio quality?
The rationale behind the comparison with a MUSHRA test is to compare any effects that are seen in the volume adjustment test with those that can be measured using the standard methodology. According to research such as [12], a MUSHRA test
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likely will not display significant results at 256 kbps, but should be able to distinguish lower bit rates. If the subjective volume test happens to display significant results at this high level, this would suggest that (1) even a bit rate of 256 kbps is affecting the listening experience, and (2) this effect can be measured by testing preferred listening levels. Such a result would lend considerable support to the Scheps hypothesis. If the listening level results mirror the MUSHRA results, that would support the assertion that perceptual coding may affect the listening experience, but that this effect is in line with what can be seen in direct, conscious ratings of audio quality, and no separate test of aesthetic enjoyment is necessary. If the listening level test does not achieve significance while the MUSHRA does, that would suggest that perceptual coding at the bit-rates tested does not have an effect on the overall listening experience, or that the subjective volume test cannot measure it.
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CHAPTER II
FIRST PHASE OF STUDY Methods
The first phase of the study was directed at the first two research questions listed above:
1. To what extent do the results of a subjective volume adjustment test reflect listener enjoyment?
2. Are some recordings better for this test than others? If so, why?
For this phase, thirteen 1-minute audio excerpts were selected to serve as
listening items. The recordings were presented in full-resolution CD quality (16bit,
44.1 kHz Wav PCM). In addition to addressing the first two research questions, this phase was used to select the three test items for the second phase of the study, which focuses on the third and fourth research questions. Both phases of the study were reviewed and approved by the Colorado Multiple Institutional Review Board, under protocol number 16-0338.
Seven people participated in the first phase. All participants were either current students in the upper division of the Recording Arts program at the University of Colorado Denver, or graduates of that program. As such, they had significant experience with critical listening to audio at the time of the test.
The recordings used for the test were taken from popular releases in several instrumental genres on the Bandcamp online store during the Winter/Spring of 2016. To qualify as a test item, a song had to meet certain criteria: be instrumental, be relatively uniform in volume, and be somewhat high-energy. Instrumental recordings
20


were chosen for two reasons: (1) if a recording has vocals that are mixed more quietly, a listener could turn up the recording merely to be able to understand the words, and (2) if a lyric happened to either resonate with or offend particular listeners, they could radically adjust their liking rating due to something from their own personal experience that is accidentally related to the audio being presented. In short, it was decided that instrumental recordings presented a purer emotional experience. Volume uniformity was important because subjects should be able to set a constant level at which they could listen to the full test item. On a very dynamic recording, a listener may want to listen to different sections at different levels, which could confound the test. Finally, the energy of the recording was important because it was hypothesized that calm, slow recordings might actually induce the subjects to turn the music down. This intuition is partly confirmed by [57], which showed that listeners prefer softer music for relaxation. For this reason, the experimenter did not include any music that sounded as if it were intended for relaxation.
Since the recordings were selected from Bandcamp, they were largely self-released by independent artists. All potential recordings were therefore screened in a professional listening room for glaring mix issues or frequency imbalances, as these could cause expert listeners to limit their listening volume. Table 2-1 lists the specific recordings used for the first study.
The long-term loudness of each recorded excerpt was measured according to EBU R128 measurement using Waves WLM meter. Item P10 was measured to be the quietest, and every other clip was subsequently turned down to match its volume. Each
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Table 2-1: Recordings chosen for first phase of study.
Item ID Artist Album Song Style Range Tested
P01 Apollo Brown Clouds "Blue Ruby Hip-Hop 0:05- 1:07
P02 Blue Cranes Swim "Everything is Going to be Okay Jazz 1:35- 2:40
P03 El Buho Y "Sayaranigracht Electronic 2:18- 3:19
P04 Vasa Colours "The Angry Dome Hard Rock 2:43- 3:46
P05 Sferro Wetware Computer "Wetware Computer Electronic 1:24- 2:26
P06 IV the Polymath When & Where "White Candles Hip-Hop 0:18- 1:21
P07 Yuka Pitfall "Sauerkraut Jazz/Funk 0:10- 1:11
P08 MM MM EP "Rope" Electronic 3:11- 4:12
P09 Microlith Hello 307 "Ju-World Electronic 0.28- 1:30
P10 Smoked Beat Locations: Japan "No" Hip-Hop 0:30- 1:32
Pll Cleft Bosh! "Hostage" Hard Rock 1:31- 2:32
P12 Eyeliner Buy Now "High Heels Electronic 0:32- 1:34
P13 Mr. Bill & Au5 Corrective Scene Surgery "Shlappy Electronic 0:50- 1:53
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item was also measured using Max to determine its spectral centroid, a measurement of
the mean frequency of a signal that is calculated using this formula:
Centroid =
Hn=o f(n)x(n) In^xCn)
where x(n) is the magnitude of frequency bin n, f(n) is the center frequency of the bin, and N is the total number of bins. This process relied on Alex Harkers Max external descriptorsrt, which performed the necessary transformation and calculations to deliver real-time measurements of spectral centroid. These real-time measurements were sampled 10 times per second using the snapshot object, and then the samples were averaged across the entire excerpt. Figure 2-1 shows the simple Max patcher; in this example, a value of 2719 Hz was returned as the average of 614 samplings of item P07. This process was performed eight times per recording, and the frequency values averaged once more to even out any irregularities in the snapshot sampling process.
The measured levels, applied gains, loudness ranges, and spectral centroids of the listening material are shown in Table 2-2. The loudest track was P01, which was mastered at -6.6 LUFS. The darkest track, as far as frequency distribution, was P10, with a spectral centroid of 1348 Hz. The brightest track was P09, at 4903 Hz. The process of informally auditioning the items returned reasonable consistency in terms of LU range, with the exception of P08, which is by far the most dynamic item.
The monitoring system in the listening environment was calibrated to reproduce pink noise at -18.0 dBFS at a sound pressure level of 80 dB SPL (C-weight,
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Figure 2-1: The Max patcher for measuring spectral centroid.
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Table 2-2: Long-term loudness, applied gain, loudness range, and spectral centroids for phase one test items.
Item ID Average Level (LUFS) Applied Gain (dB) Loudness Range (LU) Mean Spectral Centroid Measurement (Hz)
P01 -6.6 -7.7 3 1910
P02 -13.8 -0.5 3 1840
P03 -13.6 -0.7 1 3500
P04 -9.0 -5.3 5 2445
P05 -10.4 -3.9 4 3926
P06 -8.5 -5.8 2 2897
P07 -12.2 -2.1 3 2736
P08 -10.0 -4.3 12 2247
P09 -13.5 -0.8 2 4903
P10 -14.3 -0.0 3 1348
Pll -7.8 -6.5 3 3102
P12 -12.8 -1.5 2 2006
P13 -8.6 -5.7 4 3410
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slow). Subjects were instructed to treat the session as an enjoyable listening session, like one that would take place in their car or on a home stereo. All faders started at 0.0 dB, and subjects were instructed to use the Avid Artist Control surface to adjust the volume of each recording according to their preference. Latch automation was used to write the listening level onto the track in Pro Tools. Figure 2-2 shows an example of how volume automation was used to record listening level.
At the end of each excerpt, the subjects were asked if they were satisfied with their listening level, or if they wanted to listen again to make adjustments. If they made any adjustments within the last 10 seconds of the excerpt, the excerpt was played again to ensure that they had arrived at the perfect listening level. No subject ever needed to go through any excerpt more than twice. Before moving on the next item, the subject wrote down a rating on a scale from one to 10 according to how much they enjoyed that recording. Appendix A shows the rating sheet used.
All subjects were instructed to inform the experimenter if they recognized any of the test items. Two subjects thought they may have heard one or two before, but no one was able to confidently say they recognized an excerpt or identify any of the artists. After each subject finished, the experimenter measured his/her volume levels in two ways. One was using the Waves WLM plugin set to EBU R128 averaging standards.
Each item was played through at the subjects final listening level and the Long-Term LUFS value recorded. Also, in order to approximate the average sound pressure level at the listening position, the experimenter simultaneously took readings from a B&K Precision 732A Sound Level Meter. The meter was set to A-weighting and slow
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Figure 2-2: Example of the automation recorded for the first phase of the study.
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response time. The maximum and minimum SPL levels were recorded, and then the
midpoint between those two calculated using the following formula:
P = log^C
dBSPL
20
)
Midpoint dBSPL = 201og ( low ^ hlgh)
Results
Table 2-3 displays the aggregate data across all subjects and all audio items. Before this analysis, the listening levels and enjoyment ratings were transformed into deviations from each subjects mean; this corrected for variations in both how loud each subject preferred to listen to music overall, and how each subject interpreted the rating scale. These correlations show a high level of significance, while explaining close to 30% of the variance. This result confirms the data from [29]. Table 2-4 shows the analysis when performed after the data had not only been normalized with regard to the subjects means, but also to the standard deviations of their enjoyment ratings. This is performed in case some subjects were more or less likely to use the extremes of the scale. It assumes that the difference between each subjects most and least liked songs in the set should be roughly the same. This assumption may or may not be accurate, but the analysis after dividing by the standard deviations returns a similar result. Table 2-5 shows the uncorrected average results across all seven subjects, for each individual recording. Table 2-6 shows the results with each subjects data presented as a
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Table 2-3: Aggregated results from phase one of study. Values used are corrected to deviations from each subject's mean.
Interaction N P-val R2 Pearson
LUFS enjoyment 91 <0.001 0.28 0.53
dB(A) enjoyment 91 <0.001 0.33 0.57
Table 2-4: Aggregated results for phase one. Listening level values are corrected to deviations from each subject's mean. Enjoyment ratings are deviations from means that are then divided by each subject's standard deviation.
Interaction N P-val R2 Pearson
LUFS enjoyment 91 <0.001 0.18 0.43
dB(A) enjoyment 91 <0.001 0.25 0.50
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Table 2-5: Per-item correlations between listening level and enjoyment rating for phase
one. Values used are uncorrected.
Item ID LUFS R2 dB(A) R2 LUFS Pearson dB(A) Pearson LUFS P- val dB(A) P- val
P01 0.30 0.36 0.55 0.60 0.10 0.08
P02 0.25 0.22 -0.50 -0.47 0.13 0.15
P03 0.04 0.09 0.21 0.29 0.33 0.26
P04 0.30 0.28 -0.55 -0.53 0.10 0.11
P05 0.57 0.47 0.75 0.69 <0.05 <0.05
P06 0.65 0.68 0.81 0.83 <0.05 <0.05
P07 0.07 0.02 0.26 0.13 0.28 0.39
P08 0.34 0.05 0.06 -0.23 0.08 0.31
P09 0.22 0.20 0.46 0.45 0.15 0.15
P10 0.18 0.16 0.43 0.40 0.17 0.19
Pll 0.66 0.70 0.81 0.84 <0.05 <0.05
P12 0.01 0.00 0.08 0.01 0.43 0.49
P13 0.53 0.55 0.73 0.74 <0.05 <0.05
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Table 2-6: Per-item correlations between listening level and enjoyment ratings for
phase one. Values used are corrected to deviations from each subject's mean.
Item LUFS R2 dB(A) R2 LUFS Pearson dB(A) Pearson LUFS P- val dB(A) P- val
P01 0.00 0.00 0.02 -0.01 0.48 0.49
P02 0.01 0.01 -0.10 -0.11 0.41 0.41
P03 0.01 0.00 -0.12 0.04 0.40 0.47
P04 0.27 0.29 0.52 0.54 0.12 0.10
P05 0.08 0.03 0.28 0.18 0.27 0.35
P06 0.21 0.23 0.46 0.48 0.15 0.14
P07 0.81 0.80 0.90 0.89 <0.01 <0.01
P08 0.52 0.60 0.72 0.77 <0.05 <0.05
P09 0.24 0.45 0.49 0.67 0.13 0.05
P10 0.18 0.20 0.42 0.44 0.17 0.16
Pll 0.72 0.75 0.85 0.87 <0.05 <0.05
P12 0.17 0.16 0.42 0.40 0.18 0.19
P13 0.61 0.63 0.78 0.79 <0.05 <0.05
Table 2-7: The five items with the most significant correlations according to the average across raw and corrected data.
Item Average LUFS P-val Average LUFS R2 Average dB(A) P-val Average dB(A) R2
P06 0.08 0.59 0.07 0.41
P08 0.06 0.70 0.17 0.65
P09 0.14 0.23 0.10 0.33
Pll 0.01 0.69 0.01 0.73
P13 0.03 0.57 0.02 0.59
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deviation from that subjects average. Test items with significant p-values (p<0.05) are shown in bold.
Table 2-7 shows the five items with the lowest P-values when averaged across the raw correlations and the deviation data. From this analysis, P06, Pll, and P13 were chosen for the main study. These three recordings showed the strongest correlations between listening volume and listener enjoyment.
Why do some recordings correlate so much more strongly than others? The next series of tables will analyze the strength of the correlation with regard to the initial mastered level of the recordings and their spectral centroids. Each factor (mastered level and centroid) will be analyzed with regard to both the significance of the result, and the amount of variance explained (R2).
1. Does the initial mastered level of the track (LT LUFS) correlate with the p-value of the relationship between listening level and enjoyment?
As can be seen in Table 2-8, significant (p<0.05) negative correlations are seen with the correlations between raw volume data and raw enjoyment ratings. This suggests that the low p-values tended to come from tracks that were mastered at higher levels. But this correlation evaporates when using the more accurate data that was corrected as deviations from the subjects mean volumes and enjoyments.
2. For the items that displayed significant (p<0.05) correlations between listening level and enjoyment, does the initial mastered level of the track (LT LUFS) correlate with the R2 values of these correlations?
As shown in Table 2-9, none of these relationships are statistically significant.
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Table 2-8: Correlations between the initial mastered level of a track and the significance of the correlation between listening level and enjoyment rating.
Measurement N P-val R2 Pearson
LUFS raw 13 <0.05 0.42 -0.65
dB(A) raw 13 <0.05 0.32 -0.56
LUFS dev. 13 0.633 0.02 -0.15
dB(A) dev. 13 0.690 0.02 -0.12
Table 2-9: Correlations between the mastered level of a track and the amount of variance in listening level explained by variance in enjoyment rating.
Measurement N P-val R2 Pearson
LUFS Raw 4 0.478 0.27 0.52
dB(A) Raw 4 0.114 0.79 0.89
LUFS dev. 4 0.624 0.14 -0.38
dB(A) dev. 5 0.481 0.18 0.42
Table 2-10: Correlations between the spectral centroid of a track and the significance of the correlation between listening level and enjoyment rating.
Measurement N P-val R2 Pearson
LUFS raw 13 0.555 0.03 -0.18
dB(A) raw 13 0.329 0.09 -0.29
LUFS dev. 13 0.538 0.04 -0.19
dB(A) dev. 13 0.564 0.03 -0.18
Table 2-11: Correlations between the spectral centroid of a track and the amount of variance in listening level explained by variance in enjoyment rating.
Measurement N P-val R2 Pearson
LUFS Raw 4 0.303 0.49 -0.70
dB(A) Raw 4 0.053 0.90 -0.95
LUFS dev. 4 0.716 0.08 0.28
dB(A) dev. 5 0.217 0.46 -0.68
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3. Does the spectral centroid of a track correlate with the p-values of the relationship between listening level and enjoyment?
As shown in Table 2-10, none of these relationships are statistically significant.
4. For the items that displayed significant (p<0.05) correlations between listening level and enjoyment, does the spectral centroid of the track correlate with the R2 values of these correlations?
From Table 2-11, none of these items are significant at the level ofp<0.05. One is significant at the p<0.1 level, suggesting that higher spectral centroids (brighter-sounding music) may be more likely to result in weaker correlations (in terms of R2) between listening level and enjoyment ratings. But this weak correlation is only seen in the dB(A) measurement, and again disappears when moving to the data that is corrected with regard to each subjects mean.
Discussion
The answer to research question one is seen in Table 2-3. A moderately strong correlation was measured between listening level and listener enjoyment ratings, with high statistical significance. Research question two, however, has a more complicated answer. A surprising range of variation was seen in the strength of the correlations between listening level and enjoyment ratings, but there is little evidence that this variation was due to either the mastered level of the recording or its spectral centroid. One of the measurements suggested a positive relationship between mastered level and statistical significance, as shown in Table 2-8. This makes theoretical sense: if a record was mastered loud in the first place, it might feel better at a louder listening level, as the
34


frequency balance achieved at a higher phon curve will be more in line with what the mastering engineer intended. But the correlation was not seen with the more accurate mean-deviation data, so the chance of this being a statistical fluke is considerable. Also, it did not correlate with the R2 values, which seems to suggest an influence on the significance of the correlation but not its strength. Unfortunately, the analysis of R2 could only be performed on the four test items that were found to have significant correlations in the first place, and such a small sample certainly limits the conclusions that can be drawn.
Any future research that tests the interaction between volume and enjoyment should keep in mind some important methodological concerns. Correcting the statistics with regard to mean-deviation is important, and the analysis performed on the raw data should be minimal. This is the way to control for variation in how loud each subject prefers to listen to music, so that their levels can be compared across subjects. The enjoyment ratings should also be normalized in this fashion. Also, when the subject is listening and moving the fader, the volume automation should be written only for a short section of the track; this will prevent subjects from having any motivation to try to match the fader position they settled on for a previous test item. Markers in Pro Tools can be used for this purpose, as shown in Figure 3-1 below.
Future study on this topic could focus on several avenues. The present study focused on popular styles of music: hard rock, hip-hop, jazz-funk and electronic -chosen because they tend to be listened to at a louder level. Interestingly, at least one item from each genre returned significant results in either the raw or corrected data. The effect measured here has also been shown in classical [29] and world music [27]
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recordings. A future study could delve further into the influence of genre on the correlation between loudness and enjoyment.
Due to the high number of test items that did not show significant correlations, the volume adjustment methodology used in this study may need to be conducted more rigorously, possibly with more repetitions of each item, as in [27]. Yagi, Oohashi, et al. played the piece a total of six times for each data point, while the current study used fewer repetitions in an attempt to test several recordings in one session. A future study on this method could investigate the use of additional repetitions to reduce statistical noise in the data gathered by the listening level method. If the method can be refined, more research can be directed at what, if any, characteristics make a recording suitable for test using this technique.
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CHAPTER III
SECOND PHASE OF STUDY Methods
The second phase was focused on addressing research questions three and four, concerning the efficacy of the volume adjustment test for audio quality-related research. A total of 13 subjects participated in this phase of the study, which was split into three sessions: two volume adjustment sessions and one MUSHRA session. To keep the listeners from becoming too familiar with the test items during the volume adjustment section, the listening sessions were conducted with a week in between. A reference item (P09) was included to be able to correct for day-to-day fluctuation in volume preference; if a subject was simply in the mood for quiet music during one of their sessions, this effect would be seen also in the reference recording and could be corrected. P09 was selected because it was one of the least dynamic test items, as shown by the LU range measurement in Table 2-2.
The test items were assigned new IDs for the main study, as shown in Table 3-1. The three test items (not including the reference) were encoded into MP3 format using the iTunes MP3 encoder. The quality levels chosen for the test are shown in Table 3-2. All bit-rates are constant bit-rates.
Table 3-3 shows the different test conditions for Volume Session 1, as well as the number of subjects assigned to each condition. The different conditions reverse the order of the three test items within each quality level as well as the order of the two quality levels presented in each session. The assigned condition for each subject was held constant from Volume Session One to Volume Session Two, with the difference that
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Table 3-1: Recordings used in second phase of study.
Pilot ID Artist/Title Style Main ID
P09 Microlith "Ju-World Electronic Reference
Pll Cleft "Hostage Hard Rock MA
P06 IV the Polymath "White Candles Hip-Hop MB
P13 Mr. Bill & Au5 "Shlappy Electronic MC
Table 3-2: MP3 coder bit-rates used for test items in second phase.
Quality Level Item IDs
Original CD quality MAI, MB1, MCI
256 kbps MP3 MA2, MB2, MC2
160 kbps MP3 MA3, MB3, MC3
96 kbps MP3 MA4, MB4, MC4
Table 3-3: Test conditions for Volume Session One in Phase Two.
Condition Item Order N
1 Ref: MAI: MB1: MCI: MA3 : MB3 : MC3 4
2 Ref: MA3 : MB3 : MC3 : MAI: MB1: MCI 3
3 Ref: MCI: MB1: MAI: MC3 : MB3 : MA3 3
4 Ref: MC3 : MB3 : MA3 : MCI: MB1: MAI 3
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Session Two presented quality levels two and four instead of quality levels one and three.
During the volume section, the instructions for subjects were the same as in the pilot study. In order to provide more data for research question one, subjective enjoyment ratings for each of the four items were also gathered in this phase, using the same scale seen in Appendix A. Different versions of the same recording were placed on the same track, as shown in Figure 3-1. As seen in the figure, volume automation was only written for short segments of each track. This was to prevent a subject from seeing their previously written automation and attempting to match it; they approached each excerpt with no indication of the listening levels previously recorded.
One week following the second volume session, the subjects participated in the MUSHRA session. This test was performed using the Max MUSHRA test GUI from the University of Surrey Institute of Sound Recording website. The quality anchors were created using 96dB/octave low-pass filters in Fab-Filter Pro-Q. As recommended in ITU-R Bs. 1534, the mid-quality anchor was filtered above 7.5 kHz, and the low-quality anchor was filtered above 3 kHz. All subjects took the test on Sennheiser HD280 headphones, using either Avid Mbox 2 or Steinberg UR22 for their D/A convertor and headphone amplifier. Subjects were permitted to adjust their headphone volume at any point during the test. The test GUI automatically presented each of the three test items twice, for a total of six repetitions. The interface determined the order in which the items would be presented; the resulting distribution of test conditions is shown in Appendix B.
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Figure 3-1: Example of the automation recorded in Pro Tools for phase two.
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Before performing the MUSHRA test, subjects were given a training session concerning the audio artifacts that are introduced by low bit-rate coders. The two recordings used for the training were P07 and P09; the quality levels were full-resolution PCM, 160 kbps MP3, and 96 kbps MP3. Subjects discussed different artifacts under the categories of changes in timbral fidelity, spatial fidelity, and technical quality. The experimenter pointed out areas in each recording where artifacts were most noticeable, and encouraged subjects to share artifacts they noticed. After training in Pro Tools, the subjects switched over to Max 7 for the MUSHRA test interface.
Using different materials for training is a departure from the guidelines in ITU-R 1534. By using P09 and P07 for training, some consistency was achieved between the MUSHRA test and the volume adjustment test, where P09 was used as the Reference file. However, this meant that the subjects were not able to listen to the specific test items during training; they had to transfer what they heard to new music items, which likely increased the difficulty of the MUSHRA test.
Results
Volume Adjustment Test
Table 3-4 and 3-5 show the same analysis performed in phase one with the additional enjoyment data and listening levels gathered in phase two. The result seen in phase one holds and gains statistical significance from the additional data. Next, in order to compare listening values across quality levels, the value cp was defined for each test item (MA2-4, MB2-4, MC2-4) as follows (This example is for MA3):
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Table 3-4: Aggregated correlations between listening level and enjoyment ratings, both measured as deviations from a subject's mean. Includes data from phases one and two.
Interaction N P-val R2 Pearson
LUFS enjoyment 134 <0.001 0.28 0.53
dB(A) enjoyment 134 <0.001 0.28 0.53
Table 3-5: Correlations between level and enjoyment, measured as deviations from each subject's mean, for the four recordings used in both phases of the study. Data is aggregated across both phases.
Item LUFS Pearson LUFS R2 LUFS P-value dB (A) Pearson dB (A) R2 dB (A) P-value
Ref. (P09) 0.45 0.20 <0.05 0.48 0.23 <0.05
MA (Pll) 0.67 0.45 <0.01 0.75 0.56 <0.001
MB (P06) 0.72 0.51 <0.001 0.69 0.47 <0.001
MC (P13) 0.68 0.46 <0.01 0.68 0.46 <0.01
Table 3-6: Averaged difference values for phase two of the study, as well as standard deviations and p-values.
Item Mean

MA2 0.6 3.6 0.72 0.4 3.6 0.54
MB2 -0.3 2.8 0.36 -0.7 4.1 0.44
MC2 0.3 2.7 0.66 -0.2 3.3 0.48
MA3 0.6 1.7 0.91 0.8 1.7 0.62
MB3 -0.1 2.1 0.41 -0.1 2.2 0.48
MC3 0.1 2.4 0.54 0.1 2.4 0.51
MA4 0.7 3.3 0.77 0.4 3.3 0.55
MB4 -0.5 3.1 0.28 -1.0 4.0 0.41
MC4 0.7 3.3 0.78 0.1 3.7 0.51
42


MA3cp = (MA3 LUFS Reference LUFS) (MAI LUFS Reference LUFS)
Both LUFS values are corrected to represent their deviations from the LUFS value for the reference recording, and then cp represents the difference between a test items LUFS deviation and the LUFS deviation from the full-quality version of that item. A similar value i|j, was defined for the dB (A) midpoints:
MA3i|j = (MA3 dBA Reference dBA) (MAI dBA Reference dBA)
Table 3-6 shows the mean values, as well as their standard deviations and the results of a one-tailed t-test to see if they were significantly different from zero. None of these values reached statistical significance. The LUFS data for all four items, including the full-quality versions, can be visualized in Figures 3-2, 3-3, and 3-4.
The means for the four items were also compared using a one-way ANOVA. Normality was verified by analyzing skewness and kurtosis, and variances were compared to ensure that the ANOVA could be performed. The only sample that failed these tests was the LUFS measurements for MA3, which was positively skewed and had a significantly smaller variance. The skew was corrected using a logarithmic transformation, but the reader should note that the divergent variances make the ANOVA less applicable for the LUFS measurements on item MA. Table 3-7 shows the ANOVA results, which show that the variation in listening level is almost certainly due to statistical noise.
43


Item MA
20 15 10 5 0 -5 -10
MAI MA2 MA3 MA4
^^Box
Mean line
Figure 3-2: Averaged listening levels (LUFS) for item MA, corrected with regard to reference recording.
Item MB
Box
^^Mean line
Figure 3-3: Averaged listening levels (LUFS) for item MB, corrected with regard to reference recording.
44


Item MC
Figure 3-4: Averaged listening levels (LUFS) for item MC, corrected with regard to reference recording.
Table 3-7: ANOVA results for volume adjustment test.
Item/Measurement F F crit. P-value
MA/LUFS 0.13 2.80 0.94
MA/dB(A) 0.06 2.80 0.98
MB/LUFS 0.06 2.80 0.98
MB/dB(A) 0.18 2.80 0.91
MC/LUFS 0.29 2.80 0.83
MC/dB(A) 0.02 2.80 0.996
45


Table 3-8 shows the effect of listening order on the LUFS measurements of listening level, measured as deviations from the Reference LUFS. This analysis was performed on a per-day basis: the seven items presented to one subject on day 1 were separated from those presented on volume day 2. This table shows a significant, though very weak, positive correlation between listening level and song order. Listening order was also investigated on a per subject basis, and only two subjects revealed significant correlations. Their statistics are shown in Table 3-9.
After noticing that the two subjects with significant relationships displayed Pearson correlations in opposite directions, the subjects were divided into groups based on the sign of their Pearson correlations. As these two subjects were the only two with statistical significance on an individual level, putting the remaining subjects into groups means judging Pearson correlations from insignificant data. Therefore, what follows is not much more than speculation. Table 3-10 shows the analysis performed on these two groups. The negative correlation has fewer degrees of freedom, and fails to achieve significance at the p<0.05 level. The positive correlation is significant, and greater in magnitude than that seen in Table 3-8, but still weak in terms of the amount of variance explained (R2). Still, these data suggest there may be a significant difference between the two groups of listeners: people who turn the music up as they go, and people who turn it down. As shown in Table 3-11, the two groups of listeners were evenly split across the test conditions. (As a reminder, from Table 3-3 test conditions 1 and 3 had the higher quality version presented first, and 2 and 4 had it second.) Due to this even distribution, it seems unlikely that the slight correlations
46


Table 3-8: Correlation between listening order and LUFS measurements of listening level.
Pearson R2 P-value
0.15 0.02 <0.05
Table 3-9: Data from the two listening subjects whose listening levels showed significant correlations between item order and listening level.
Subject Pearson R2 P-value
1 -0.71 0.51 <0.01
2 0.75 0.57 <0.01
Table 3-10: Statistical analysis of subjects grouped by the sign (+/-) of the correlation between item order and listening level.
Group N Pearson R2 P-value
Positive 9 0.28 0.08 <0.01
Negative 4 -0.24 0.06 0.07
Table 3-11: Distribution of subjects according to test conditions and listening order correlation groups.
Group/Condition Positive Negative
High Quality First (1 & 3) 4 2
High Quality Second (2 & 4) 5 2
47


between listening level and item order confounded any measurable relationship between listening level and audio quality.
MUSHRA Test
The raw data from the MUSHRA sessions are shown in the Appendix. According to the ITU standard, data from each subject were screened according to how well that subject distinguished the audio items. The standard states that any subject who rated the hidden reference below a score of 90 for more than fifteen percent of the test items was not able to identify the coding artifacts and should have their results screened from the analysis. Unfortunately, when this standard is applied to the present data, 11 of the 13 subjects have their results screened. For two reasons, it was decided that this standard could be relaxed to allow more data to be analyzed: first, the training session was conducted using different audio material than the testing session, which increased the difficulty of the test, and second, the subjects only evaluated six items, so the fifteen percent threshold is crossed after a mistake on just one of them. For these reasons, the screening standard was set at any subject who rated more than two hidden references below a score of 80. This still screens 4 of the 13 subjects. In observations of the training phase, two of these four subjects expressed preferences for the low bit-rate versions and struggled to hear the artifacts pointed out by the experimenter. The other two subjects who fail the screening test took noticeably less time than the others on both training and testing; their inaccuracy when identifying the hidden reference item may have been due to this lack of thoroughness.
The data were also screened for significant outliers, defined according to the lower and upper bounds indicated in the ITU standard:
48


Lower bound = Q1 1.5 IQR
Upper Bound = Q3 + 1.5 IQR
where Q1 is the first quartile, Q3 the third quartile, and IQR the Inter-Quartile Range. Only one of the screened outliers was removed from the analysis, as it corresponded with an observed abnormality from the test session. One subject rated the last hidden reference at an 11 after identifying the majority of the other references, which is a significantly low outlier. This particular subject took longer than average to complete the session, and was noticeably tired at the end. The extremely low rating on the reference was therefore regarded as a result of fatigue or pressure to finish, and discarded. The following figures and tables show the data from the MUSHRA session, after discarding the screened subjects and the outlier data point. Clearly, the mid- and low-quality anchors were rated much worse than the other items. This is borne out in the one-way ANOVA performed on the data, as shown in Table 3-15.
In this case, however, several of the items failed the tests for normality: MA3 and MB4 were negatively skewed, and MB1 and MCI were both negatively skewed and platykurtic. These issues could not be corrected using logarithmic or square-root transformations. The ANOVA test is usually robust to some deviations from normality, but some of the contrasting variances (e.g. MA3, MB4) require that the results of this test be regarded with some skepticism. With these caveats in mind, the pairwise mean
49


Item MA
Figure 3-5: MUSHRA audio quality ratings for Item MA.
Box
^^Mean line
Table 3-12: Means and variances for MUSHRA audio quality ratings of Item MA.
Item N Mean rating Variance
MAI 18 89.94 203.58
MA2 18 85.72 205.62
MA3 18 84.78 355.12
MA4 18 68.67 186.82
MA5 18 21.78 119.01
MA6 18 12.72 112.80
50


Item MB
Figure 3-6: MUSHRA audio quality ratings for Item MB.
Box
Mean line
Table 3-13: Means and variances for MUSHRA audio quality ratings of Item MB.
Item N Mean rating Variance
MB1 18 95.72 103.27
MB2 18 80.67 163.65
MB3 18 85.22 108.42
MB4 18 77.33 340.24
MB5 18 24.39 100.49
MB6 18 13.89 90.46
51


Item MC
100
80
60
40
20
Reference 256kbps 160kbps 96kbps Mid Low
Box
^^Mean line
Figure 3-7: MUSHRA audio quality ratings for Item MC.
Table 3-14: Means and variances for MUSHRA audio quality ratings of Item MC.
Item N Mean rating Variance
MCI 17 93.24 172.94
MC2 18 87.67 94.35
MC3 18 85.17 215.79
MC4 18 78.83 212.26
MC5 18 26.17 183.56
MC6 18 16.17 130.26
52


Table 3-15: One-way ANOVA results for MUSHRA data.
Item F F crit. P-value
MA 158.06 2.27 <0.001
MB 191.24 2.27 <0.001
MC 130.53 2.27 <0.001
Table 3-16: Pairwise comparisons with reference recording for all MUSHRA items.
Pairwise Comparison Difference of Means P-value
MA1-MA2 4.22 0.37
MA1-MA3 5.17 0.27
MA1-MA4 21.28 <0.001
MA1-MA5 68.17 <0.001
MA1-MA6 77.22 <0.001
MB1-MB2 15.06 <0.001
MB1-MB3 10.5 <0.05
MB1-MB4 18.39 <0.001
MB1-MB5 71.33 <0.001
MB1-MB6 81.83 <0.001
MC1-MC2 5.57 0.21
MC1-MC3 8.07 0.07
MC1-MC4 14.40 <0.05
MC1-MC5 67.07 <0.001
MC1-MC6 77.07 <0.001
53


comparisons across all test items can be seen in Table 3-16, with the statistically significant ones in bold.
In summary, these data suggest that the subjects could consistently identify the quality degradation from the 96 kbps MP3 encoder, but that the two higher quality MP3s were not picked out as consistently. Interestingly, item MB shows significant differences for all 5 pairwise comparisons, but the 256 kbps MP3 was rated somewhat lower than the 160 kbps file. This is a curious result that suggests a potential fluke. Overall, these results indicate that the subjects displayed slightly lower acuity in distinguishing low bit-rate versions than the subjects in [12], who were able to distinguish recordings as high as 192 kbps.
Discussion
There was no difference in the mean listening levels across the four audio quality levels tested, which answers research question three in the negative. The present study joins [51], [52] by Schoeffler and Herre in failing to find any correlation between audio bit rate and listener enjoyment. With regard to question four, the MUSHRA test was a much more reliable method of testing audio quality, even with a small number of subjects who were given a slightly more difficult task because of the adjusted training phase. From these results, one of three things must be concluded: either [1] the listening level test conducted here lacked the rigor necessary to measure differences in aesthetic enjoyment, [2] the subjective volume adjustment test in general is not adequate to this task, or [3] perceptual coding down to 96 kbps does not have a significant effect on listener enjoyment, and the Scheps hypothesis is contradicted. The least likely of these three conclusions appears to be the second: the results from the
54


first phase of this study, in addition to [27], [29] suggest that subjective listening level is a reasonable measurement of listener enjoyment. The first possibility, that the study reported herein was not controlled tightly enough, merits the following consideration.
As mentioned in the discussion for the first phase, this study differed from previously successful implementation of the behavioral listening level test [27] in key ways. The present study repeated the test items fewer times, and it tested more than one piece of music per session. These two factors were related in the planning of the study: because listeners were hearing four different songs, it was expected that they might compare the songs with each other. Then, as the subjects became more familiar with the music, it was feared that their volume levels would have more to do with this value judgment based on their past experience than with the quality of the audio. For this reason, the number of repetitions of each song was kept to a minimum. All of this is important because it may have affected the precision of the volume measurements. As shown in [27], the volume differences can be very slight with this test, and a high level of rigor as well as a large number of test subjects may be important. Another key difference that may have affected the precision of the test was the fact that data was aggregated across two separate volume sessions. The use of a reference recording was intended to normalize the data from the two days, but may have added some noise to the measurement.
Maximizing rigor in this test seems to be somewhat at odds with controlling the influence of familiarity, the importance of which was discussed earlier. But as long as listening order is controlled for, a more tightly run test could be performed by focusing on only one recording and asking each subject to set their listening volume several
55


times for each quality level. To keep the listening sessions from becoming too long, the test should perhaps be confined to one full-quality recording and one average-quality MP3, such as 128 kbps. If such a test joined the present study in failing to find a significant difference between the recordings, that could be taken as strong evidence against the Scheps hypothesis and against the thesis of The Distortion of Sound [15].
56


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60


APPENDIX A
RATING SHEET USED FOR BOTH PHASES OF STUDY
1 2 3 4 5 I 6 7 I 8 9 I 10
Make it stop I dislike this This is OK I like this My new favorite song
Song #1
Song #2
Song #3
Song #4
Song #5
Song #6
Song #7
Song #8
Song #9
Song #10
Song #11
Song #12
Song #13
61


APPENDIX B
DISTRIBUTION OF TEST CONDITIONS FOR MUSHRA TEST
Randomly generated test condition N
MA : MB : MB : MC : MA : MC 1
MA : MB : MA : MB : MC : MC 1
MA:MC:MA:MB:MB:MC 1
MB:MA:MA:MB:MC:MC 1
MB:MB:MC:MA:MC:MA 2
MB:MC:MA:MA:MB:MC 1
MB : MC : MA : MA : MC : MB 2
MC:MA:MB:MC:MB:MA 2
MC:MA:MC:MB:MA:MB 1
MC:MC:MB:MA:MB:MA 1
62


APPENDIX C
RAW DATA Phase One
Uncorrected
Item LUFS dBAMP Rating
P01 -23.3 70.9 6
-27.5 66.6 4
-19.8 74.5 4
-18.3 76.9 7
-18.2 77.2 6
-25.9 69.5 6
-15.5 79.3 7

P02 -27.6 67.7 7
-21.8 73.8 5
-22.6 73.0 6
-15 82.5 7
-17.4 80.4 5
-30.1 67.9 7
-16.5 81.3 5

P03 -24.5 72.6 7
-22.1 75.5 6
-33.1 63.0 4
-17.7 80.6 6
-15.4 83.0 8
-29.6 70.0 8
-15.8 82.5 5

P04 -22.2 73.7 7
-21.4 74.7 9
-24.2 72.2 8
-14.4 82.7 7
-23.5 73.5 8
-20.3 77.0 8
-16.4 80.2 7

P05 -24.8 73.9 7
-21.5 77.2 8
-28.2 70.7 5
-17.3 82.2 7
-17.5 82.8 8
-21.8 79.0 6
-16.8 83.2 8

P06 -25.2 75.3 7
-25.6 75.1 7
-28.8 70.7 3
-15.5 85.2 8
-13.1 88.9 9
-20.3 81.5 7
-17.7 83.6 7

P07 -22.8 76.0 7
-18.9 80.1 10
-23.8 74.5 9
-16.5 83.4 6
-16.8 83.5 8
-27.8 72.6 5
-16.1 84.3 7

P08 -22.8 74.3 8
-23.8 73.4 6
-23 73.9 7
-13.6 84.7 6
-14.3 84.0 8
-22.2 75.9 7
-20.8 82.3 5

P09 -23.6 69.7 8
-29.2 64.5 5
-32.9 61.1 3
63


-18 77.0 5
-18.6 76.7 7
-24.6 67.2 4
-16.5 78.8 5

P10 -26.8 70.0 8
-23.2 73.9 5
-23.2 74.2 8
-13.3 84.8 8
-14.3 83.8 9
-23.6 74.9 6
-17.3 80.8 7

Pll -20.7 77.0 8
-21.9 75.9 8
-30.8 65.3 2
-12.2 85.9 8
-15 83.9 10
-22.2 76.7 7
-16.3 82.3 7

P12 -24.1 71.5 7
-25.7 69.7 6
-23.5 71.8 8
-12.7 83.9 6
-16.8 79.9 8
-24.5 72.2 3
-15.2 81.6 5

P13 -28.8 68.7 6
-28.7 68.6 1
-35.2 62.0 1
-14.3 84.4 7
-17.1 82.4 8
-27.9 71.0 6
-18.2 81.7 5

Corrected to Deviations from Means
Item LUFS dev. dBAMP dev. Rating dev.
P01 1.10 -1.54 -1.15
-3.55 -6.43 -2.15
7.05 4.77 -1.23
-3.01 -5.71 0.23
-1.43 -4.38 -1.85
-1.22 -3.99 -0.15
0.94 -2.39 0.85

P02 -3.20 -4.69 -0.15
2.15 0.80 -1.15
4.25 3.24 0.77
0.29 -0.16 0.23
-0.63 -1.15 -2.85
-5.42 -5.58 0.85
-0.06 -0.42 -1.15

P03 -0.10 0.19 -0.15
1.85 2.46 -0.15
-6.25 -6.73 -1.23
-2.41 -2.07 -0.77
1.37 1.49 0.15
-4.92 -3.51 1.85
0.64 0.79 -1.15

P04 2.20 1.28 -0.15
2.55 1.66 2.85
2.65 2.38 2.77
0.89 0.10 0.23
-6.73 -8.06 0.15
4.38 3.48 1.85
0.04 -1.45 0.85

64


P05 -0.40 1.49 -0.15
2.45 4.22 1.85
-1.35 0.97 -0.23
-2.01 -0.47 0.23
-0.73 1.30 0.15
2.88 5.46 -0.15
-0.36 1.53 1.85

P06 -0.80 2.88 -0.15
-1.65 2.13 0.85
-1.95 0.97 -2.23
-0.21 2.58 1.23
3.67 7.31 1.15
4.38 7.98 0.85
-1.26 1.89 0.85

P07 1.60 3.59 -0.15
5.05 7.07 3.85
3.05 4.74 3.77
-1.21 0.74 -0.77
-0.03 2.00 0.15
-3.12 -0.86 -1.15
0.34 2.62 0.85

P08 1.60 1.89 0.85
0.15 0.40 -0.15
3.85 4.10 1.77
1.69 2.08 -0.77
2.47 2.47 0.15
2.48 2.43 0.85
1.04 0.59 -1.15

P09 0.80 -2.65 0.85
-5.25 -8.46 -1.15
-6.05 -8.69 -2.23
-2.71 -5.60 -1.77
-1.83 -4.84 -0.85
0.08 -6.31 -2.15
-0.06 -2.89 -1.15

P10 -2.40 -2.39 0.85
0.75 0.93 -1.15
3.65 4.46 2.77
1.99 2.14 1.23
2.47 2.22 1.15
1.08 1.42 -0.15
-0.86 -0.86 0.85

Pll 3.70 4.61 0.85
2.05 2.94 1.85
-3.95 -4.45 -3.23
3.09 3.32 1.23
1.77 2.40 2.15
2.48 3.24 0.85
0.14 0.61 0.85

P12 0.30 -0.93 -0.15
-1.75 -3.32 -0.15
3.35 2.02 2.77
2.59 1.24 -0.77
-0.03 -1.61 0.15
0.18 -1.26 -3.15
1.24 -0.06 -1.15

P13 -4.40 -3.74 -1.15
-4.75 -4.39 -5.15
-8.35 -7.78 -4.23
0.99 1.80 0.23
-0.33 0.85 0.15
-3.22 -2.50 -0.15
-1.76 0.04 -1.15

65


Phase Two
Volumes Uncorrected
Subject Excerpt LUFS dB(A) MP Rating
1 Ref 13 -26.6 71.9 7
MAI -27.4 70.3 5
MB1 -26.6 71.9 7
MCI -25.5 71.6 8
MA3 -24.6 73.5
MB3 -27.8 70.6
MC3 -29.4 67.5
Ref24 -34.6 64.9
MA2 -26.4 71.9
MB2 -31.8 68.1
MC2 -32.5 65.7
MA4 -30.9 67.0
MB4 -35.9 63.8
MC4 -32.2 66.0

2 Ref 13 -20.7 73.4 6
MAI -19.6 78.5 7
MB1 -18.2 81.3 7
MCI -20.4 77.1 5
MA3 -18.3 79.7
MB3 -17.7 81.6
MC3 -20.8 76.6
Ref24 -20.8 73.8
MA2 -17.7 80.6
MB2 -17.5 82.0
MC2 -19.5 78.5
MA4 -17.7 81.0
MB4 -17.4 82.6
MC4 -18.4 79.5

3 Ref 13 -15.2 78.9 7
MAI -12.9 84.9 10
MB1 -14 84.9 9
MCI -15.1 82.5 8
MA3 -13.5 84.6
MB3 -12.4 87.0
MC3 -16.7 81.1
66


Ref24 -16.4 78.0
MA2 -13.7 84.3
MB2 -15.9 82.9
MC2 -16 81.6
MA4 -13.2 85.0
MB4 -14.4 84.5
MC4 -16.4 80.9

4 Ref 13 -30 63.6 5
MAI -20.3 77.6 8
MB1 -24.7 74.2 7
MCI -31.5 66.1 1
MA3 -19.1 79.1
MB3 -22.5 76.3
MC3 -25.8 71.3
Ref24 -31.5 63.1
MA2 -19.6 78.7
MB2 -26.2 74.0
MC2 -30.8 67.5
MA4 -18.2 80.2
MB4 -22.7 77.2
MC4 -26.3 71.8

5 Ref 31 -17.9 76.3 5
MAI -16.1 81.8 7
MB1 -17.7 80.7 7
MCI -18.1 78.1 5
MA3 -15.9 82.3
MB3 -17.2 81.7
MC3 -18.3 78.7
Ref42 -14 80.9
MA2 -12.5 85.5
MB2 -13.9 86.0
MC2 -16.8 81.4
MA4 -13.3 85.0
MB4 -13.8 85.9
MC4 -16.4 81.2

6 Ref 31 -22.2 72.2 5
MAI -17.2 81.4 8
MB1 -18.4 81.3 6


MCI -21.9 76.2 4
MA3 -17.2 81.3
MB3 -22.4 77.4
MC3 -21 77.1
Ref24 -20.7 73.4
MA2 -17.8 80.4
MB2 -17.7 81.5
MC2 -19.5 78.0
MA4 -18.7 79.4
MB4 -17.6 81.6
MC4 -21.9 75.4

7 Ref 31 -20.3 74.3 7
MAI -20.8 78.9 5
MB1 -14.3 88.6 8
MCI -18.2 81.7 5
MA3 -19.2 80.8
MB3 -18.8 84.1
MC3 -20.8 79.1
Ref42 -18.8 76.2
MA2 -17.6 81.1
MB2 -16.8 82.4
MC2 -19.2 77.7
MA4 -18.4 80.1
MB4 -17.3 81.7
MC4 -18.3 78.7

8 Ref 31 -20.9 76.2 7
MAI -14.3 85.2 9
MB1 -26.7 75.5 5
MCI -22.3 77.2 8
MA3 -14.7 84.9
MB3 -28.3 73.0
MC3 -25.5 74.0
Ref24 -23.1 71.9
MA2 -17 82.4
MB2 -22.8 79.1
MC2 -22.1 76.7
MA4 -17 81.8
MB4 -24.9 75.0
MC4 -26.8 70.1



9 Ref 31 -21.3 73.3 6
MAI -25.3 73.2 7
MB1 -24.7 74.8 7
MCI -24.7 71.8 4
MA3 -21.5 77.1
MB3 -23.3 76.3
MC3 -23.4 73.0
Ref24 -22.8 72.7
MA2 -25.2 74.0
MB2 -26.1 75.5
MC2 -25.5 73.0
MA4 -23.3 76.1
MB4 -26.2 75.6
MC4 -23.2 75.5

10 Ref 13 -20.3 74.9 7
MAI -18.2 81.4 6
MB1 -16.1 85.8 8
MCI -20.3 79.6 9
MA3 -19.1 80.6
MB3 -15.6 85.9
MC3 -19.8 80.5
Ref24 -30.4 64.3
MA2 -24.6 73.9
MB2 -27.3 72.8
MC2 -24.2 73.1
MA4 -22.1 76.7
MB4 -29.1 71.1
MC4 -24.3 73.5

11 Ref 13 -22.1 73.0 7
MAI -19.7 80.0 6
MB1 -21.4 80.8 7
MCI -24 75.5 5
MA3 -20.1 79.8
MB3 -19.3 82.9
MC3 -22.8 77.0
Ref24 -18.1 76.5
MA2 -17.6 80.7
MB2 -18.8 80.8


MC2 -21.7 75.2
MA4 -17.8 80.6
MB4 -18 81.7
MC4 -20.1 76.9

12 Ref 13 -23.8 71.7 8
MAI -17.5 81.1 8
MB1 -22 78.8 8
MCI -23.6 74.6 5
MA3 -17.1 81.8
MB3 -20.2 80.6
MC3 -22.9 75.2
Ref24 -22.2 73.0
MA2 -17.2 81.5
MB2 -20.3 80.4
MC2 -21.1 76.9
MA4 -16.7 81.9
MB4 -18.8 82.0
MC4 -19.7 78.9

13 Ref 13 -20.8 74.6 5
MAI -15.9 83.3 3
MB1 -13.5 87.8 7
MCI -16.9 82.3 4
MA3 -18.5 80.9
MB3 -13.5 87.7
MC3 -16.5 82.9
Ref24 -25.1 69.4
MA2 -26.9 71.3
MB2 -23.4 76.1
MC2 -25.9 70.9
MA4 -25.5 72.6
MB4 -25.1 74.5
MC4 -25.4 71.5



Level Differences with the Reference Recording
Subject Excerpt LUFS dB(A) MP
1 Refl3 - -
MAI -0.8 -1.6
MB1 0.0 0.0
MCI 1.1 -0.3
MA3 2.0 1.6
MB3 -1.2 -1.3
MC3 -2.8 -4.4
Ref24 - -
MA2 8.2 7.0
MB2 2.8 3.2
MC2 2.1 0.8
MA4 3.7 2.1
MB4 -1.3 -1.0
MC4 2.4 1.1

2 Refl3 - -
MAI 1.1 5.0
MB1 2.5 7.8
MCI 0.3 3.7
MA3 2.4 6.3
MB3 3.0 8.2
MC3 -0.1 3.2
Ref24 - -
MA2 3.1 6.8
MB2 3.3 8.2
MC2 1.3 4.6
MA4 3.1 7.2
MB4 3.4 8.8
MC4 2.4 5.7

3 Refl3 - -
MAI 2.3 6.0
MB1 1.2 5.9
MCI 0.1 3.6
MA3 1.7 5.7
MB3 2.8 8.1
MC3 -1.5 2.2
Ref24 - -
MA2 2.7 6.3
MB2 0.5 4.8
MC2 0.4 3.5
MA4 3.2 6.9
MB4 2.0 6.4
MC4 0.0 2.8

4 Refl3 - -
MAI 9.7 14.0
MB1 5.3 10.6
MCI -1.5 2.6
MA3 10.9 15.5
MB3 7.5 12.7
MC3 4.2 7.7
Ref24 - -
MA2 11.9 15.6
MB2 5.3 10.8
MC2 0.7 4.4
MA4 13.3 17.1
MB4 8.8 14.1
MC4 5.2 8.7

5 Ref31 - -
MAI 1.8 5.4
MB1 0.2 4.4
MCI -0.2 1.8
MA3 2.0 6.0
MB3 0.7 5.4
MC3 -0.4 2.4
Ref42 - -
MA2 1.5 4.6
MB2 0.1 5.1
MC2 -2.8 0.4
MA4 0.7 4.0
MB4 0.2 5.0
MC4 -2.4 0.3

6 Ref31 - -
MAI 5.0 9.2
71


MB1 3.8 9.2
MCI 0.3 4.0
MA3 5.0 9.1
MB3 -0.2 5.2
MC3 1.2 4.9
Ref24 - -
MA2 2.9 7.0
MB2 3.0 8.1
MC2 1.2 4.6
MA4 2.0 6.0
MB4 3.1 8.2
MC4 -1.2 2.0

7 Ref31 - -
MAI -0.5 4.6
MB1 6.0 14.3
MCI 2.1 7.4
MA3 1.1 6.6
MB3 1.5 9.8
MC3 -0.5 4.8
Ref42 - -
MA2 1.2 4.9
MB2 2.0 6.2
MC2 -0.4 1.5
MA4 0.4 3.8
MB4 1.5 5.5
MC4 0.5 2.5

8 Ref31 - -
MAI 6.6 9.0
MB1 -5.8 -0.6
MCI -1.4 1.1
MA3 6.2 8.7
MB3 -7.4 -3.2
MC3 -4.6 -2.1
Ref24 - -
MA2 6.1 10.5
MB2 0.3 7.2
MC2 1.0 4.7
MA4 6.1 9.8
MB4 -1.8 3.1
MC4 -3.7 -1.8

9 Ref31 - -
MAI -4.0 -0.1
MB1 -3.4 1.5
MCI -3.4 -1.5
MA3 -0.2 3.7
MB3 -2.0 3.0
MC3 -2.1 -0.3
Ref24 - -
MA2 -2.4 1.4
MB2 -3.3 2.8
MC2 -2.7 0.3
MA4 -0.5 3.4
MB4 -3.4 2.9
MC4 -0.4 2.9

10 Refl3 - -
MAI 2.1 6.5
MB1 4.2 10.9
MCI 0.0 4.7
MA3 1.2 5.6
MB3 4.7 11.0
MC3 0.5 5.6
Ref24 - -
MA2 5.8 9.6
MB2 3.1 8.5
MC2 6.2 8.8
MA4 8.3 12.4
MB4 1.3 6.8
MC4 6.1 9.2

11 Refl3 - -
MAI 2.4 7.0
MB1 0.7 7.8
MCI -1.9 2.5
MA3 2.0 6.8
MB3 2.8 9.9
MC3 -0.7 4.0
Ref24 - -
MA2 0.5 4.3
72


MB2 -0.7 4.3
MC2 -3.6 -1.2
MA4 0.3 4.1
MB4 0.1 5.2
MC4 -2.0 0.5

12 Refl3 - -
MAI 6.3 9.5
MB1 1.8 7.1
MCI 0.2 3.0
MA3 6.7 10.1
MB3 3.6 9.0
MC3 0.9 3.6
Ref24 - -
MA2 5.0 8.5
MB2 1.9 7.4
MC2 1.1 4.0
MA4 5.5 9.0
MB4 3.4 9.0
MC4 2.5 5.9

13 Refl3 - -
MAI 4.9 8.8
MB1 7.3 13.3
MCI 3.9 7.8
MA3 2.3 6.4
MB3 7.3 13.2
MC3 4.3 8.3
Ref24 - -
MA2 -1.8 1.9
MB2 1.7 6.6
MC2 -0.8 1.5
MA4 -0.4 3.1
MB4 0.0 5.1
MC4 -0.3 2.1

73


MUSHRA test
The item IDs correspond with those in the volume adjustment phase. Items 5 and 6 in each set denote the mid- and low-quality low-pass-filtered anchors. Subjects whose data was screened as described in the text are shown in italics and shading, as is the one outlier that was omitted from analysis.
Subject Item Rating Item Rating Item Rating
1 MAI 95 MB1 100 MCI 95
MA2 60 MB2 90 MC2 95
MA3 100 MB3 90 MC3 90
MA4 70 MB4 90 MC4 80
MA5 20 MB5 30 MC5 25
MA6 10 MB6 10 MC6 10

MAI 100 MB1 100 MCI 100
MA2 95 MB2 84 MC2 90
MA3 95 MB3 84 MC3 95
MA4 70 MB4 90 MC4 92
MA5 19 MB5 25 MC5 11
MA6 10 MB6 10 MC6 13

2 MAI 80 MB1 100 MCI 100
MA2 100 MB2 83 MC2 80
MA3 82 MB3 84 MC3 70
MA4 40 MB4 81 MC4 77
MA5 9 MB5 22 MC5 33
MA6 2 MB6 20 MC6 8

MAI 100 MB1 100 MCI 81
MA2 100 MB2 90 MC2 84
MA3 80 MB3 80 MC3 100
MA4 68 MB4 80 MC4 80
MA5 25 MB5 24 MC5 26
MA6 20 MB6 11 MC6 19

3 MAI 100 MB1 100 MCI 100
MA2 80 MB2 68 MC2 69
MA3 67 MB3 79 MC3 90
74


MA4 49 MB4 50 MC4 51
MA5 30 MB5 31 MC5 30
MA6 8 MB6 11 MC6 11

MAI 100 MB1 100 MCI 89
MA2 70 MB2 71 MC2 100
MA3 89 MB3 89 MC3 52
MA4 48 MB4 50 MC4 67
MA5 28 MB5 30 MC5 30
MA6 7 MB6 13 MC6 13

4 MAI 41 MB1 100 MCI 68
MA2 99 MB2 76 MC2 82
MA3 60 MB3 79 MC3 89
MA4 85 MB4 85 MC4 100
MA5 2 MB5 11 MC5 4
MA6 4 MB6 1 MC6 4

MAI 100 MB1 52 MCI 91
MA2 79 MB2 85 MC2 100
MA3 80 MB3 100 MC3 87
MA4 82 MB4 89 MC4 81
MA5 2 MB5 5 MC5 6
MA6 2 MB6 2 MC6 4

5 MAI 68 MB1 100 MCI 98
MA2 54 MB2 54 MC2 95
MA3 98 MB3 85 MC3 66
MA4 60 MB4 58 MC4 57
MA5 40 MB5 28 MC5 37
MA6 36 MB6 30 MC6 39

MAI 69 MB1 100 MCI 98
MA2 83 MB2 75 MC2 75
MA3 98 MB3 78 MC3 91
MA4 62 MB4 59 MC4 74
MA5 38 MB5 37 MC5 38
MA6 36 MB6 36 MC6 36

6 MAI 100 MB1 60 MCI 100
MA2 80 MB2 81 MC2 100
75


MA3 40 MB3 91 MC3 60
MA4 71 MB4 100 MC4 71
MA5 10 MB5 20 MC5 20
MA6 5 MB6 5 MC6 6

MAI 91 MB1 100 MCI 51
MA2 100 MB2 60 MC2 100
MA3 40 MB3 89 MC3 91
MA4 87 MB4 86 MC4 91
MA5 20 MB5 30 MC5 21
MA6 4 MB6 10 MC6 11

7 MAI 95 MB1 100 MCI 100
MA2 95 MB2 100 MC2 90
MA3 100 MB3 100 MC3 90
MA4 85 MB4 90 MC4 90
MA5 25 MB5 35 MC5 51
MA6 10 MB6 20 MC6 30

MAI 94 MB1 85 MCI 98
MA2 98 MB2 94 MC2 98
MA3 94 MB3 100 MC3 100
MA4 86 MB4 90 MC4 100
MA5 35 MB5 35 MC5 50
MA6 20 MB6 20 MC6 32

8 MAI 100 MB1 100 MCI 100
MA2 80 MB2 72 MC2 80
MA3 80 MB3 80 MC3 75
MA4 85 MB4 90 MC4 80
MA5 20 MB5 19 MC5 28
MA6 20 MB6 20 MC6 20

MAI 80 MB1 100 MCI 75
MA2 100 MB2 80 MC2 80
MA3 80 MB3 80 MC3 80
MA4 75 MB4 80 MC4 100
MA5 28 MB5 30 MC5 19
MA6 19 MB6 20 MC6 19

9 MAI 89 MB1 60 MCI 70
76


MA2 79 MB2 80 MC2 52
MA3 100 MB3 100 MC3 72
MA4 49 MB4 40 MC4 100
MA5 30 MB5 26 MC5 34
MA6 16 MB6 11 MC6 29

MAI 68 MB1 100 MCI 93
MA2 88 MB2 70 MC2 66
MA3 100 MB3 89 MC3 100
MA4 48 MB4 100 MC4 76
MA5 31 MB5 18 MC5 33
MA6 12 MB6 12 MC6 18

10 MAI 84 MB1 82 MCI 73
MA2 79 MB2 92 MC2 84
MA3 100 MB3 100 MC3 67
MA4 59 MB4 82 MC4 73
MA5 19 MB5 29 MC5 28
MA6 8 MB6 12 MC6 15

MAI 66 MB1 76 MCI 100
MA2 89 MB2 77 MC2 87
MA3 82 MB3 100 MC3 81
MA4 84 MB4 76 MC4 78
MA5 21 MB5 36 MC5 17
MA6 11 MB6 15 MC6 15

11 MAI 79 MB1 69 MCI 95
MA2 58 MB2 85 MC2 37
MA3 100 MB3 78 MC3 68
MA4 60 MB4 96 MC4 62
MA5 11 MB5 4 MC5 16
MA6 9 MB6 3 MC6 10

MAI 79 MB1 96 MCI 98
MA2 95 MB2 68 MC2 80
MA3 87 MB3 77 MC3 78
MA4 86 MB4 82 MC4 64
MA5 3 MB5 3 MC5 11
MA6 1 MB6 2 MC6 2

77


12 MAI 52 MB1 86 MCI 100
MA2 78 MB2 100 MC2 80
MA3 100 MB3 74 MC3 94
MA4 60 MB4 36 MC4 100
MA5 2 MB5 2 MC5 1
MA6 1 MB6 2 MC6 1

MAI 100 MB1 100 MCI 11
MA2 79 MB2 77 MC2 77
MA3 86 MB3 58 MC3 100
MA4 73 MB4 80 MC4 67
MA5 3 MB5 1 MC5 3
MA6 1 MB6 1 MC6 1

13 MAI 100 MB1 100 MCI 100
MA2 91 MB2 80 MC2 91
MA3 100 MB3 93 MC3 89
MA4 79 MB4 85 MC4 77
MA5 23 MB5 20 MC5 18
MA6 13 MB6 3 MC6 7

MAI 95 MB1 92 MCI 100
MA2 100 MB2 93 MC2 94
MA3 97 MB3 100 MC3 100
MA4 68 MB4 97 MC4 65
MA5 17 MB5 20 MC5 30
MA6 7 MB6 8 MC6 15
78


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