TRACKING THE EVOLUTION OF A BAND’S LIVE PERFORMANCESOVER DECADES
Florian Thalmann, Eita Nakamura, Kazuyoshi Yoshii
Graduate School of Informatics, Kyoto University, Japan
thalm007@umn.edu, {eita.nakamura, yoshii}@i.kyoto-u.ac.jp

ABSTRACT
Evolutionary studies have become a dominant thread in theanalysis of large audio collections. Such corpora usuallyconsist of musical pieces by various composers or bands
and the studies usually focus on identifying general historical trends in harmonic content or music production techniques. In this paper we present a comparable study thatexamines the music of a single band whose publicly available live recordings span three decades. We ﬁrst discuss
the opportunities and challenges faced when working withsingle-artist and live-music datasets and introduce solutions for audio feature validation and outlier detection. We
then investigate how individual songs vary over time andidentify general performance trends using a new approachbased on relative feature values, which improves accuracy
for features with a large variance. Finally, we validate our
ﬁndings by juxtaposing them with descriptions posted in
online forums by experienced listeners of the band’s large
following.
1. INTRODUCTION
Music information retrieval has proven essential for the
analysis of large audio corpora, especially ones for which
traditional music analysis methods are limited. Such cases
include large audio collections for which there is no appropriate symbolic transcription, such as ones containing
non-western music or improvised music [1–5].
In recent years numerous studies have characterized thetemporal evolution of musical characteristics in such corpora. Serra et al identiﬁed a restriction of pitch transitions,homogenization of timbral palette, and growing loudness
levels in Western popular music [6]. On a similar corpus,
Mauch et al discovered three stylistic revolutions between
1960 and 2010, based on topics identiﬁed via latent Dirichlet allocation of harmonic and timbral features [7]. Deruty
and Pachet determined the ‘loudness war’ in popular music production to have peaked in 2007 [8]. Weiss et al
found harmonic complexity to be gradually increasing in
Jazz solos between the 1920s and the 2000s [4]. Weiss et© F. Thalmann, E. Nakamura, and K. Yoshii. Licensed under a Creative Commons Attribution 4.0 International License (CC BY
4.0). Attribution: F. Thalmann, E. Nakamura, and K. Yoshii, “Tracking
the Evolution of a Band’s Live Performances over Decades”, in Proc. ofthe 23rd Int. Society for Music Information Retrieval Conf., Bengaluru,
India, 2022.

850

al also conﬁrmed common hypotheses concerning the evolution of chord transitions, intervals, and tonal complexity
in Western classical music [9]. Parmer and Ahn measured
information-theoretic complexity of pitch, loudness, timbre, and rhythm in a popular music dataset and identiﬁed
trends over decades [10]. 1
All of these studies look at music at a social or culturallevel and use audio corpora that consist of material by various composers or musicians, as well as of different genres,subgenres, instrumentations, etc. In this paper we present
an analogous study which however focuses on the music ofa single band, the Grateful Dead, who are well-known for
their ever-evolving performances. We use a dataset thatconsists of audio recordings of 2617 performances of 15
songs spanning three decades. Although this may be a sizable dataset for a band, we ﬁnd that musical characteristics
show a large variance over the relatively short timespan.
For example, the tempos of the performances in the set
range from 50 to 160, which over the whole time span results in a relatively sparse cloud of data points.
Previous approaches are relatively limited when dealing with such diverse data. They all consider the corpus asa whole and simply plot the evolution of audio features
against time. This may work reasonably well for somemusical characteristics such as tonal complexity, timbre,
or loudness. However, we show how subdividing the corpus into subsets, in our case songs, and considering feature
data relative to these subsets before integrating them into awhole can improve accuracy and conﬁdence for the detection of overall trends. We identify such trends in various
performance characteristics and juxtapose them with observations by experienced listeners from the band’s large
following. In particular, despite working with a relatively
small subset of only 15 of the band’s songs, we are able
to identify generally perceived trends in tempo, song duration, and dynamic, spectral and harmonic content withpromising accuracy.
2. DATASETThe cultural impact and decades long performance historyof the Grateful Dead has led to continued interest in the
band’s music by both fans and scholars [14]. The bandis especially known for their free and inclusive approach
to music, their unwillingness to bow to the conventions of
popular music, and their aspiration to provide their fans
1 Similar trend analyses were recently done with non-audio music collections, such as with lyrics [11] or symbolic data [12, 13].

Proceedings of the 23rd ISMIR Conference, Bengaluru, India, December 4-8, 2022with a new experience at every concert. The performancesof their songs often vary greatly, even from day to day, andthey often engage in long improvisational parts or jams.
Almost all of their over 2000 concerts between 1965 and1995 have been recorded, often multiple times, and mostof these recordings are public domain and included in theLive Music Archive’s (LMA) largest subcollection. 2 To
musicologists, this catalog may be intimidating for thesereasons [15], but this make it all the more interesting forstudies using MIR methods.
We use a dataset introduced by [5] 3 which includes
2617 performed versions of a set of 15 songs from theGrateful Dead collection of the Live Music Archive. According to the authors, the songs were selected based on
two criteria: a large number of versions with soundboard
recordings across the whole time span, as well as a studiorecording as a potential reference. Many of these recordings contain crowd noise, which may affect the quality offeatures, and many are out of tune due to varying tapespeed during recording. The dataset comes with a scriptthat downloads the ﬁles from the LMA and automaticallyresamples them based on tuning ratios determined fromchroma vectors. Figure 1 (a) shows a chronological distribution of the ﬁles. We observe very low counts for theﬁrst two years which may be due to a lower number ofavailable recordings, and 1975 when the band retired for ayear. A relative distribution of songs across the years (Figure 1 (b)) shows that the ﬁrst two years of the dataset only
contain one song, which may be problematic for an evolutionary analysis. A more systematic generation of such adataset may prove useful in the future, but we chose to use
it here without modiﬁcations.
3. METHOD
The fact that we have subsets of identical or similar musical pieces or recordings can be leveraged at different points
in the process. First, a large number of audio features
are extracted for each recording in the corpus, now tuned
as described above. We then validate these features relatively for each song, which allows us to detect outliers,
i.e. wrongly classiﬁed songs, as well as adjust wrongly extracted features such as double-time beats. Our statisticalanalysis includes two steps. We ﬁrst analyze the feature
distribution and evolution for each song independently,
which allows us to characterize the relative evolution of
normalized feature values for each song. These relative
evolutions are then collated into a global evolution curvefor each feature, which we validate using bootstrapping,i.e. by alternately leaving out each song.
3.1 Feature Extraction
The set of audio features used in our study were inspiredby previous work on other corpuses referenced in Section 1and extracted using madmom 4 (beats, from which we de-version count

200

150

estimated_prophet
eyes_of_the_world
franklin's_towerscarlet_begonias
ship_of_fools
sugar_magnolia
truckin'

100

50

0

1965

1970

1975

1980
time

1985

1990

1995

1985

1990

1995

(a)
1.0

proportion

0.8

0.6

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0.0

1965

1970

1975

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time

(b)

Figure 1: (a) Chronological distribution of the recordings
for each song in the dataset. (b) Relative distribution ofsongs by year, order and colors correspond to (a).
rived tempo), librosa 5 (chroma and mfcc, summarized tothe madmom beats), as well as the essentia freesound extractor 6 (for dynamic and spectral summary features). We
used standard settings for all extractors, except for withmadmom’s DBNBeatTrackingProcessor where we used amuch higher transition lambda of 2000 rather than the suggested maximum of 300 in the documentation. This was toreduce the probability of the processor jumping between
tempos within one audio ﬁle, which may have been occurring due to the many long versions (median over 10min,
sometimes over 30min), as well as the amount of crowd
noise.
We also calculated a few additional features, inspired byother studies. Tonal complexity as deﬁned by Weiss et al as
the angular deviation or spread of pitch-classes on a circular chroma vector, permuted to correspond to the circle ofﬁfths [4,9,16]. To measure pitch content agnostic of tonality, we devised an analogous pitch complexity feature based

2 https://archive.org/details/GratefulDead

3 https://github.com/grateful-dead-live/
fifteen-songs-dataset
4 https://madmom.readthedocs.io

box_of_raincasey_jones
china_cat_sunflower
china_doll
cosmic_charlie
cumberland_blues
dancin'_in_the_street
dark_star

5 https://librosa.org

6 https://essentia.upf.edu/freesound_extractor.
html

851

Proceedings of the 23rd ISMIR Conference, Bengaluru, India, December 4-8, 2022

on the reverse-sorted normalized summarized chroma vector, which results in a right-skewed distribution, of which
we took the mean. From the 36-bin harmonic pitch classproﬁles (HPCP) extracted by essentia, we derived a tuning
complexity feature, which expresses the average deviationfrom 440Hz in both directions. 7
3.2 Beat Correction and Outlier Detection
The extracted features allow us to address two frequently
occurring problems. The ﬁrst problem is that beat tracking
is often inconsistent between different versions of a song,due to the large variance in tempo and the high amountof improvisation in the dataset. The second problem isthat some song sets may contain incomplete fragments orrecordings of other songs, due to mis-annotations in theLive Music Archive, as noted by Page et al [17].
Our beat correction method consists of identifying instances of beat features that should rather be doubletime, half-time, or two-thirds-time. 8 We start with theoriginal beat pairings Bbi = (Ri , bRi ) of recordings Ri ,i = 1 . . . K, and their corresponding beat sequences bRi ,which are simply sequences of time points, as extracted bya feature extractor. For each beat sequence bRk we create
three variants, a linearly interpolated double-time version
dRk , 9 a half-time version hRk which only contain every
other beat, and a two-thirds-time version tRk which containevery third beat of dRk . We end up with four sets of K beatsequence pairings Bbi , Bdi , Bhi , Bti , deﬁned analogously to
Bbi . For each of these pairings we extract a set of features
f1 , . . . fF which all depend on the beat sequence in thepairing. For each feature f with a corresponding distancefunction δf , we then create four K ∗ K distance matrices
x,f
Dij= δf (Bxi , Bbj ), one for each x ∈ {b, d, h, t}. Thesematrices express how well the four types of beat pairings
match with the original pairings bRi . We normalize thesematrices using min-max normalizations and calculate foreach pairing Bxk its average distance from the original pairings over all featuresx,f∆kx = µf =f1 ...fF ,j=1...K (Dkj)

Finally, we choose for each recording Rk its best pairing Bxk with minimum ∆kx .
The set of features that worked well for the dataset usedin this paper are chord sequence distributions, onset distributions, and tempo. We generate a chord sequence distribution from a summarized chord sequence, where for every beat interval we take the chord that occupies the longest
duration. From this sequence we gather all subsequencesof length L at step size 1 and count the occurrences of eachpossible pattern. Onset distributions are generated by calculating the position of each onset o relative to its neigh7 These measures are also related to the information-theoretic complexity measures used by Serra et al or Parmer and Ahn [6, 10].
8 Two-thirds-time has proven to be useful in situations where we havea ternary meter, such as 6/8, which might be interpreted as either binary
or ternary by the beat extractor.
9 Each successive pair of beats is interspersed with an average of thetwo, and an additional beat is added at the end, at an interval corresponding to last interpolated one.

852

boring beats b< , b> , e.g (o − b< )/(b> − b< ) and quantizing the resulting relative onsets to a grid of G values
by multiplying them by G rounding to the nearest integer. Both kinds of distributions are normalized. Tempo
is simply calculated as the average distance between successive beats. For beat correction, we chose the parameters
L = 4, G = 64.
For distance functions δf we used the chi-square dis2
P
i)tance χ2 (x, y) = 12 i (xxii−y+yi for distributions, and the2analogous (x−y)
x+y for two single values x, y, such as for
tempo.
Next, we identify outlier recordings using an extension of the feature set above, by adding chord sequence
distributions with L = 2 as well as overall beat count,i.e. the length of the beat feature vector. Similar to thebeat correction method, we calculate a distance matrixf
Dij= δf (Ri , Rj ) for each feature f and normalize it. We
then calculate a normalized distance df,k for each feature,
consisting of the difference between the average deviationof Rk from the overall average deviation µ(Df ), normalized by overall standard deviation σ(Df ).
df,k =fµj=1...K (Dkj
) − µ(Df )σ(Df )

Finally, we consider recordings as outliers if they deviate by 2.5 standard deviations in at least two features(df,k > 2.5) or by four standard deviations in at leastone (df,k > 4). The latter condition particularly ensuresthat recordings of extreme length, such as incomplete fragments or improperly segmented ﬁles that include morethan one song, get excluded due to an extreme deviationof overall beat count.
We obtained the best results by applying beat correction
and outlier detection successively and iteratively, i.e. one
after another in a loop, until both the beat positions and theset of included recordings are stable. Especially for songs
with less regular structure, the process only terminates after several iterations, due to the reference beat sequencesbRj changing at each step. Furthermore, with iterative application we can catch cases of quadruple time etc.
When applying this method to the dataset, a total of 211
outliers were removed (around 8%), including for a casewhere we identiﬁed a set consisting of recordings of twosongs with similar titles, possibly due to wrong annotations. Of the beat features of the remaining versions, 99
were identiﬁed as half-time, 8 as two-thirds-time, and 21
as double-time.
3.3 Statistical Analysis
We are now ready to start our diachronic analysis of thefeature data, which is structured as follows. For each feature we have one data point per version of every song.
Each version is associated with a speciﬁc day on which
it was played.
f we thus have the data points
S f,1 For featurePf =P . . . P f,15 where P f,i = (pf,iki )ki =1...Ki is
the sequence of data points for song i with Ki versions.
Although our dataset consists of material from only one
band, we face a relatively high variation in features, dueProceedings of the 23rd ISMIR Conference, Bengaluru, India, December 4-8, 2022
box_of_raincasey_jones
china_cat_sunflower

170

dancin'_in_the_street
dark_star
estimated_prophet

eyes_of_the_world
franklin's_towerscarlet_begonias

ship_of_fools
sugar_magnolia
truckin'160
140
relative deviation

160
tempo

china_doll
cosmic_charlie
cumberland_blues

150
140

120
100
80
60

130
50

100

150
version

200

250

300

Figure 2: The chronological sequence of tempo data
points of all 322 versions of Sugar Magnolia.
to the constant experimentation and improvisation of theband. Figure 2 illustrates how much the tempo of thesong Sugar Magnolia varies over time, sometimes by asmuch as 30% from one day to the next. We can identify
a global trend in such data by creating a trend line using
LOWESS [18], 10 which ﬁts a polynomial regression line
at each point, based on a local neighborhood. This neighborhood consists of a ﬁxed proportion of all points, which
we set to f = .2 or 20%. LOWESS works particularly
well for data that is unevenly distributed along the x axis,
which is the case here due to the uneven distribution ofsongs across time (Figure 1).
We can calculate such curves for each song individually, as shown in Figure 3. The top-most curve in thisﬁgure corresponds to the line shown in Figure 2 and we
can identify two tempo peaks, one around 1973 and one
around 1983. It is also apparent that the overall tempo
range in the collection varies by more than a factor of 3,
from around 50 to almost 160. In order to get a sense of
how the tempo changes over all songs, we can create a plot
for the whole set of songs, LOWESS(P f ). However, inorder to make sure that the curve is not dominated by thevalues of a single song, we calculate a conﬁdence interval
11
using bootstrapping, leaving out each song
S once.f,i For
each j = 1 . . . 15 we calculate LOWESS( i6=j P ), anddetermine at each time point the minimum and maximum
value among these curves to get the conﬁdence interval.
With absolute values, this conﬁdence interval turns out
to be quite large, leaving us unable to draw conclusions
with certainty, as shown by the orange curve in Figure 4.
For example, we cannot conﬁdently say that the peak observed in 1984 is higher than the one in 1972, due to theoverlapping conﬁdence intervals. However, we can get amuch better estimate of how the tempo changes by taking
relative feature values, which we can simply calculate by
10 As used by Moss et al in their analysis of the evolution of the distribution of pitch-class content on the line of ﬁfths [13].
11 An alternative method for this is bootstrap resampling, where we
could ensure an equal number of points per song. However, due to limitations found in the dataset (after outlier detection one song only has 22
versions), we chose to use the present method.

1968

1972

1976

1980
1984
time

1988

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1996

Figure 3: LOWESS plots of tempo for each individual
song in the dataset.
tempo relative
tempo absolute

1.3
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relative deviation

0

1.1
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0.7
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1972

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1980
1984
time

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Figure 4: Bootstrapped LOWESS curves on the wholedataset for absolute and relative tempo values.
normalizing the sequences (pf,iki ) as follows:′(pf,iki ) =(pf,iki )µ(P f,i )In the case of tempo, each version of every song now
has a relative tempo feature, which expresses how its
tempo relates to all other performances of the song. The
blue curve in Figure 4 is a bootstrapped LOWESS calculated on relative tempo values. We can observe a much narrower conﬁdence interval, now enabling us to conﬁdently
claim that in 1984 the tempo was higher than any time inthe 70s or 90s. We can also observe that in the beginningof the timeline the curves digress dramatically and witha much larger bootstrapping interval, which is due to thedataset containing few versions of few songs during thattime, as observed in Section 2. All subsequent plots are of
relative features and calculated as just described.
4. RESULTS
There has been very little musicological work on the Grateful Dead [19, 20], perhaps due to the intimidating size of853

Proceedings of the 23rd ISMIR Conference, Bengaluru, India, December 4-8, 2022
tempo
duration

1.3

relative deviation

1.2
1.1
1.0
0.9
0.8
0.7
1968

1972

1976

1980
1984
time

1988

1992

1996

Figure 5: Bootstrapped LOWESS curves for tempo andsong duration across all songs.
the band’s catalog as well as the intentionally ﬂeeting nature of the performances, as hypothesized by Tift [15].
However, when it comes to in-depth knowledge of the
band’s music, the best source are undoubtedly the band’sfans themselves, the Deadheads. Many of them have spentdecades listening to the music and debating it online, andsome of them are so knowledgeable that they have an acutesense of how the music changes every single year throughout the band’s history. In order to ﬁnd such general yet
detailed descriptions of the evolution of the band’s music
we searched the Grateful Dead subreddits, 12 the largest
forums of their kind with a total of 174k members at thetime of writing. We manually read through all pages withdiscussions on the differences between the band’s phases
and collected all general descriptions that include the musical characteristics we study, including tempo, timbre, songlengths, etc, with references to speciﬁc years. The pages
were found using combinations of the search keywords description, year, decade, 60s, 70s, 80s, 90s, progress, tempo,
jam. Some of the most detailed year by year descriptions found are by Wolfman92097, WesternEstatesHOA,
MrCompletely, and an anonymous deleted user [21–24],
and some people, such as maximinus-thrax have listened
to, rated, and brieﬂy described every of the band’s morethan 2300 shows [25, 26]. There are also many pages discussing the evolution of individual songs, such as [27].
We will now discuss our results while referring to relevant
statements from the community where we can observe a
consensus.
Figure 5 juxtaposes different temporal aspects of themusic. We can identify an overall increase in tempo in
the 1980s of more than 10%, and a subsequent decrease in
the middle of the decade. In terms of duration, the differences are even more dramatic, a sharp increase in the early70s, followed by a gradual decrease by more than 20% until the late 80s, when duration starts increasing again. Notethat the early curve segments in all plots, until about 1969,
can be considered biased for the reasons stated in Sec12 https://www.reddit.com/r/gratefuldead/, https://www.reddit.com/r/grateful_dead/854

tion 3.3. These observations coincide well with a notion in
the community that, compared to the 70s, the early 80s areperceived as more energetic, faster paced, and containing
fewer and less extensive jams. Wolfman92097 notes thatin 1980 “the tempo [starts] to speed up a little”, in 1984
“musically the tempo has really sped up”, and in 1985 “theband slows the tempo a little.” [22] It is striking that thisincrease and decrease directly corresponds to the tempo
curve in Figure 5. Although a bit more abstractly, leedye
similarly highlights that exact time period: “79-84 stands
out to me as the disco/cocaine era ... post 84 just seems to
be a little more of the slow churned vanilla as opposed tothat sweet mint chocolate chip.” [28] The same perception
is true for individual songs. Discussing a recording of thesong Eyes of the World, melwarren says “my 4 year old
asked why EOTW was going so fast” and whenthattrainrollsby replies “They played it too fast for my taste in the
80’s.” [29] On a different page the forum members observe
how the song went back to a refreshingly new slow tempo
in 1990 [27]. These observations can also be veriﬁed in
Figure 3.
In terms of song duration, braney86 notes that the “late70s was full of monster extended jams, and Jerry was absolutely on ﬁre”, while MrCompletely says that “82 through85 is a long uneven slide down ... 87 is the comeback,shows are very different, most are tightly executed, very
light on jams ... 88 starting to stretch back out a little.” [23] Wolfman92097 also observes that in late 1986
“the setlists get much longer and way more experimental than they had been all year” and that in 1988 “jazzand extreme psychedelia gets added in.” [22] Many deadheads’ favorite years are 73/74, “the peak of their spacey,
jazzy, psychedelic extended jams” according to devlinontheweb [30]. These observations again directly correspondto the plot in Figure 5. Song duration peaks in 1973, decreases throughout the late 70s and the early 80s with aturnaround point around 87/88, as perceived by MrCompletely.
Figure 6 shows plots for dynamics features. We can observe a long peak in overall loudness of the soundboard
recordings, spanning the entire 1980s. With increasing
loudness we see a decrease in dynamic range, which may
hint at an increased use of compression in the live mix.
However, when comparing with Figure 5, we can also
see a striking correlation between loudness and tempo, as
well as between dynamic complexity and duration. Dynamic complexity may be another indicator of the amountof improvisation in the recordings, similar to song duration. The latter two, however, seem to be somewhat independent nonetheless. We can particularly observe a bulge
in dynamic complexity in the late 70s which does not occur in the duration curve, and in the 90s duration increases
while dynamic complexity decreases. On the other handincreased loudness may be a direct consequence of higher
tempo and energy. EvilLinux admits that “If I am alone in
the car, I usually will choose the 80’s. There is just moreenergy, more off the rails.” [31] An anonymous deleteduser also says that “the Dead did play some incredibleProceedings of the 23rd ISMIR Conference, Bengaluru, India, December 4-8, 2022
1.4

1.08

loudness
dynamic complexity

1.06
1.04

1.2

relative deviation

relative deviation

1.3

1.1
1.0

1.00
0.98

0.94
1968

1972

1976

1980
1984
time

1988

1992

spectral complexity
spectral entropy
dissonance

1.15

0.92

1996

Figure 6: Bootstrapped LOWESS curves for dynamic essentia features (loudness_ebu128.short_term.median anddynamic_complexity) across all songs.

relative deviation

1.02

0.96

0.9
0.8

pitch complexity
tuning complexity
tonal complexity

1.10
1.05
1.00

1968

1972

1976

1980 1984
time

1988

1992

1996

Figure 8: Bootstrapped LOWESS curves for pitch, tuning
and tonal complexity features.
and tuning complexities run roughly in parallel and the period of 1980-84 is again demarcated, with a clear dip in all
three features. This may again be due to the more streamlined faster performances and the lower degree of improvisation during this period. The late 1980s show parallels
with the late 1970s, which may be related to the band starting to improvise more again. However, pitch and tuning
complexities also seem highly correlated with dissonance(Figure 7) which may be related to timbre and the typicalrich distorted and synthesizer-heavy 1980s Grateful Deadsound.

0.95
1968

1972

1976

1980 1984
time

1988

1992

1996

Figure 7: Bootstrapped LOWESS curves for spectral essentia features (spectral_complexity.median, spectral_entropy.median and dissonance.median).
shows in the early to mid-80s. They had a "fatter" sound,especially Jerry” [24].
A similar observation can be made for spectral features(Figure 7). Complexity and entropy increase for the entire
duration of the 1980s, while dissonance has a shorter peakin the late 80s. Many listeners agree that the 80s have anentirely different sound, to a large part characterized by the
keyboarder Brent Mydland who played with the band from1979 to 1990 and who used a greater variety of keyboards
and many synthesizers. According to BeaverMartin, Brent“really adds a whole different texture to the vocals andkeys,” [28] and WesternEstatesHOA says that “In 1983 theband truly takes off. The physical change of Brent’s new
keyboard is enough to change the band’s sound alone. It
has deep watery effects and adds so much depth to some of
the more simple tunes.” [22] Wolfman92097: “83 Garcia[guitar] is a little more distorted and Brent is using more
fake keyboard sounds Phil [bass] is loud.” [21]As for tonal content, Figure 8 shows a selection of features calculated as described in Section 3.1. Tonal, pitch,

5. CONCLUSION
We have shown how evolutionary methods can not only be
used for the study of general cultural trends in music, butalso to investigate how the performances of a band changeover time. We have also seen how we can improve theprediction accuracy of musical trends by considering thefeature values relative to subsets of the data. It may be possible to apply a similar method in other situations, such as
when studying the simultaneous evolution of the music ofdifferent composers, considering the music of each of them
relative to their own work. We have also discovered limitations with the dataset used and suggest, in future work,
to design a larger more systematic one in order to conﬁrm
our preliminary discoveries in this paper. Finally, while
we have shown the potential reliability of the accounts ofexperienced listeners in the community, a more systematiccollection and processing of online forum data may lead tomore detailed results in the future.
6. ACKNOWLEDGMENTS
This work is supported in part by JST PRESTO No.
JPMJPR20CB and JSPS KAKENHI Nos. 19H04137,
20K21813, 21K02846, 21K12187, 22H03661.
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