CHORUS-PLAYLIST: EXPLORING THE IMPACT OF
LISTENING TO ONLY CHORUSES IN A PLAYLIST
Kosetsu Tsukuda
Masahiro Hamasaki
Masataka Goto
National Institute of Advanced Industrial Science and Technology (AIST), Japan{k.tsukuda, masahiro.hamasaki, m.goto}@aist.go.jp
ABSTRACT
When people listen to playlists on a music streaming service, they typically listen to each song from start to end inorder. However, what if it were possible to use a function
to listen to only the choruses of each song in a playlist one
after another? In this paper, we call this music listening
concept “chorus-playlist,” and we investigate its potential
impact from various perspectives such as the demand andthe objectives for listening to music with chorus-playlist.
To this end, we conducted a questionnaire-based online
user survey involving 214 participants. Our analysis results
suggest reusable insights, including the following: (1) Weshow a high demand for listening to existing playlists withthe chorus-playlist approach. We also reveal preferred options for chorus playback, such as adding crossfade transitions between choruses. (2) People listen to playlists withchorus-playlist for various objectives. For example, when
they listen to their own self-made playlists, they want toboost a mood or listen to music in a specific context suchas work or driving. (3) There is also a high demand forplaylist creation on the premise of continuous listening to
only the choruses of the songs in a playlist. The diversities of artists, genres, and moods are more important whencreating such a playlist than when creating a usual playlist.
1. INTRODUCTION
The chorus of a song is one of the most distinctive parts inthe song. In terms of acoustic aspects, it has been reported
that the chorus tends to have louder sound, contain heavier instrumentation and additional vocals, and include thehighest-pitch vocal note in a song [1–3]. In terms of cognitive aspects, the chorus tends to be the catchiest, mostmemorable, and most salient part of a song for emotional
expression [4–7]. Moreover, the chorus is often characterized by the property of being a song’s most repeated section [8, 9]. Because of these characteristics, the chorus has
attracted academic attention. For example, research has
been conducted on music structure analysis including chorus detection [8–35] and its use for music summary generation [36, 37]. In addition, music datasets specializing inchoruses have been made publicly available [38].
© K. Tsukuda, M. Hamasaki, and M. Goto. Licensed undera Creative Commons Attribution 4.0 International License (CC BY 4.0).
Attribution: K. Tsukuda, M. Hamasaki, and M. Goto, “Chorus-Playlist:
Exploring the Impact of Listening to Only Choruses in a Playlist”, in
Proc. of the 24th Int. Society for Music Information Retrieval Conf.,
Milan, Italy, 2023.

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As for general music listening habits beyond choruses, the amount of time spent listening to music throughplaylists on music streaming services has increased [39–42]. It has also been reported that this listening time islonger than the time of listening to music via albums [43].
On the services, both playlists that are created by generalusers and those that are created by professional curators or
automatically generated are widely available [42, 44–46].
As a result, Spotify has over 4 billion playlists, for example [42]. With the popularity of playlists, many studies
have been conducted on playlist recommendation, generation, and analysis [40, 42, 44, 47–76].
Given the importance of choruses and playlists, we focus on a listening approach in which only the choruses ofthe songs in a playlist are played one after another. Certain smartphone music player applications such as Vocacolle App by DWANGO Co., Ltd., MIXTRAX App by Pioneer Corporation, and KENWOOD Music Control haveprovided a function to continuously play the choruses ofsongs or parts including the choruses. However, there has
been no academic discussion on the impact of this listening
approach. In this paper, we refer to the concept of continuous listening to only the choruses of songs in a playlist
as “chorus-playlist.” This concept can be applied not only
when a user listens to playlists that she created but also
when she listens to playlists created by other users. Under
this concept, a user could create a playlist on the premise ofcontinuous listening to only the choruses of the playlist’s
songs. Hence, the goal of this paper is to reveal the usefulness of chorus-playlist and provide reusable insights.
To achieve this goal, we conducted a questionnairebased online user survey involving 214 participants. Ourcontributions can be summarized as follows.
• To our knowledge, this is the first study investigating
the impact of continuous listening to only the chorusesof songs in a playlist.
• We reveal user preferences for chorus playback in aplaylist (e.g., users prefer to add crossfade transitions
between choruses). We also show a high demand andcertain user objectives for listening to playlists withchorus-playlist. For example, people often want to listen to their own self-made playlists to boost a mood orin a specific context such as work or driving.
• We show that people tend to be willing to create aplaylist for continuously listening to only the chorusesof the songs in the playlist. We also reveal important
properties in creating such playlists (e.g., the diversityof moods, genres, and artists).

Proceedings of the 24th ISMIR Conference, Milan, Italy, November 5-9, 2023

• According to the survey results, we suggest new research topics for the music information retrieval (MIR)
community (e.g., song recommendation for users wholisten to music with chorus-playlist). We also make several proposals for music streaming platforms to attract
users (e.g., when using the chorus-playlist approach,people would be willing to listen to playlists containing hit songs to efficiently check them out).
• We have made a portion of the survey results publicly
available on the web to support future studies 1 .
2. RELATED WORK
2.1 Chorus Analysis and Detection
The chorus is distinctive compared to other sections of asong in terms of acoustic, structural, and cognitive aspects.
In terms of acoustic aspects, it has been known that choruses tend to be louder, to contain heavier instrumentation and more vocals, and to have the highest-pitch vocal
note in a song [1–3]. More recently, Balen et al. [77] revealed that choruses have a smaller dynamic range and agreater variety of MFCC-measurable timbres, as comparedto other sections. Regarding structural aspects, choruses
are usually repeated more than other sections such as intros and verses [8, 9]. As for cognitive aspects, choruses
are the catchiest and most memorable sections for listeners [4, 5, 7]. For artists, too, choruses are distinctive inthat they are the most salient sections for emotional expression [6]. Given these characteristics, we focus here on
choruses as song excerpts, rather than other sections. As
we will show in section 7, the chorus is more preferredthan other sections for continuous listening in a playlist.
Because of the importance of choruses, many studies
have addressed musical structure analysis, including chorus detection, based on music audio signals [8–15, 17–24,
26,27,29–32,34,35] or lyrics [16,25,28,33]. The accuracyof identifying the chorus section is approximately 80-90%
in terms of the F-measure [12,22,29,33]. Accordingly, thefeasibility of implementing the chorus-playlist concept onmusic streaming services is sufficiently high. For songs inwhich the correct chorus cannot be detected, it is also possible to have users on a service manually correct choruses
through a collective intelligence approach [78].
2.2 Playlist Analysis and Recommendation
On today’s music streaming services, playlists have become a central way to listen to music [40–42]. The majority of playlists on services are created by general users
rather than professional music enthusiasts [44]. Users create playlists not only for their own listening but also toshare their musical preferences with other users such as
friends and followers [44, 53, 70]. It has also been reported that playlists can be characterized by certain properties [40, 62, 68, 69, 74, 76] such as song order [42] and low
diversity in terms of both artists and genres [70]. In thispaper, too, we report objectives for listening to music withchorus-playlist (section 5) and important properties for creating a playlist to continuously listen to only the chorusesof songs in the playlist (section 6).
Although playlists are actively created by users, it istime consuming to manually create a playlist [44]. To easethe process, two approaches have been studied: assisted
playlist creation [49–52, 54, 55, 71] and automatic playlist
generation [47, 48, 56, 58–61, 63–67, 72, 73, 75]. In song
recommendation for a playlist or generation of a playlist,
it is typical to consider the song order and/or the audio
similarity between songs. Furthermore, there are several
studies that automatically extract prominent sections (notonly limited to choruses) from individual songs and generate DJ mixes [79] or medleys [80] by connecting them.
Music streaming services such as Spotify and Deezer provide functions for automatic playlist generation to promote
song discovery by users [45, 46]. In this paper, according
to our survey results, we discuss not only new approaches
for these research topics but also how to encourage users
on music streaming services to more actively interact withplaylists and discover novel songs.
3. PARTICIPANTS
We recruited participants for our survey via an online research company in Japan. We limited the participants to
those who are Japanese, listened to music an average of atleast one day per week via any music streaming service,and had created at least 10 playlists on the service. We
paid 51.6 USD (7,000 JPY) to each participant. Although
222 participants answered the questionnaire in sections 4,
5, 6, and 7 through a web browser, to make the analysis
results more reliable, we removed the answers from eightparticipants who submitted improper responses to a freeresponse question. The remaining 214 participants were
diverse in both gender and age range: 89 males (10s: 1;
20s: 29; 30s: 35; 40s: 17; 50s: 7), and 125 females (10s:
1; 20s: 45; 30s: 38; 40s: 25; 50s: 16).
4. PREFERENCE FOR CHORUS PLAYBACK
4.1 Chorus Playback Choices
As explained in section 1, the concept of chorus-playlist
enables a user to continuously listen to only the choruses ofsongs in a playlist. However, some users may prefer to add
crossfade transitions between choruses. In this section, weinvestigate the preferences for chorus playback in chorusplaylist in terms of the following three choices.
• TimePreChorus: the playback time before the chorus.
The options are “no playback,” “5 seconds,” and “10seconds.” “No playback” means that only the choruses
are continuously played, without any part of the song
before the chorus.
• Crossfade: whether 1-second crossfade transitions areadded between songs. The options are “on” and “off.”• TimeChorus: the playback time for the chorus. The
options are “15 seconds,” “30 seconds,” and “adaptive.”
In the case of “15 (resp. 30) seconds,” 15 (resp. 30)
seconds on a song is played from the beginning of the

1 The data can be downloaded from https://github.com/
ktsukuda/chorus_playlist.

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Proceedings of the 24th ISMIR Conference, Milan, Italy, November 5-9, 2023

first chorus 2 . In the case of “adaptive,” the first chorus
is played from beginning to end regardless of its length.
Hereafter, we refer to a combination of these three choicesin the form of a triplet such as (TimePreChorus, Crossfade,
TimeChorus) = (5 seconds, off, adaptive).
4.2 Dataset
In this survey, the participants listened to playlists that we
provided. To reduce bias due to the played songs, we used
120 songs created by professional musicians that we commissioned. That is, we guaranteed that the participants had
never listened to any of the 120 songs. Instead of using
chorus detection methods introduced in section 2.1, a music expert manually labeled the start and end times of eachsong’s first chorus to prevent detection errors. For 26 songs
that started with the chorus, the expert labeled the start andend times of the second chorus, because the mood and/orbeat of such leading choruses are sometimes different fromthose of the second and subsequent choruses. The averageand standard deviation of chorus lengths for the 120 songs
were 29.0 and 7.66 seconds, respectively, and 52 songs hadchoruses longer than 30 seconds.
As the participants had various music preferences, wecreated diverse playlists by sampling the songs to be included in playlists as follows. First, the 120 songs wereplotted in the valence-arousal (VA) space according to theiraudio features. Next, we applied the k-means algorithmand classified the 120 songs in the VA space into three clusters. We created three playlists by sampling five songs at
random for each playlist from one cluster. Similarly, we
created three more playlists from another cluster. Finally,
we created three playlists containing diverse songs in termsof their moods by randomly selecting one song from eachof the two previous clusters and three songs from the remaining cluster. Each playlist’s song order was also determined randomly. In total, we created nine playlists thateach comprised five songs. Note that there were no songoverlaps between any pairs of playlists.
For each playlist, there were 18 total option combinations (3 options for TimePreChorus × 2 options for Crossfade × 3 options for TimeChorus). For example, for (5seconds, on, adaptive), we first created an MP3 file by cutting each song in a playlist from five seconds before thefirst chorus to the end of the chorus and then connecting
the songs with crossfade transitions. We then created an
MP4 file to simulate the participants’ experience of listening to the playlist on a smartphone application. Specifically, given an MP3 file, we created an MP4 file in which
the MP3’s playlist was played and images changed at thesame time the song changed in the playlist. Each image
contained a song’s title and artist name like in the music
playback screen of a music player application (Figure 1) 3 .
4.3 Procedure
First, we investigated the participants’ preferred option
combinations. To this end, three dropdown lists with the
2 Thus, if the first chorus is less than 15 (resp. 30) seconds long, thesong continues play after the chorus until the total playback time reaches
15 (resp. 30) seconds.
3 The images also include icons of pause, next, and previous buttons.

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Playback time before the chorus.
No playback
Crossfade transition between tracks.
On

Playlist 1

Playlist 2

Playlist 3

MISTAKE

Jelly fish

Cinderella GirlAury

JD Walker

Katy S

Playback time of the chorus.
30 seconds

Figure 1. Interface example from our user survey. In thisexample, when a participant selects the options (no playback, on, 30 seconds), three playlists satisfying this combination are displayed.
options for each choice were presented to the participants.
For each participant, three playlists were selected at random from the set of playlists described in section 4.2. Oncethe participant had selected an option for each choice, thethree playlists (MP4 files) satisfying that option combination were displayed (Figure 1). When the participantchanged the combination, the displayed playlists were alsochanged to those satisfying the new option combination 4 .
After listening to playlists for any option combination,
the participants reported their most preferred combinationsuch as (10 seconds, on, 30 seconds), by selecting those
options from the dropdown lists 5 .
Even if a participant chose “adaptive” as the preferred
option for TimeChorus, she may have liked “30 seconds”
almost as much. Accordingly, after the above investigation, we investigated the option preferences including such
subtle differences. To this end, we displayed each of the
18 options with a six-point Likert scale ranging from “notpreferred at all” to “very preferred,” and we asked the participants to rate their preferences for each option.
4.4 Results
Table 1 and Figure 2 indicate the results for the first andsecond investigations, respectively. In Table 1, we cansee that the most popular combination was (5 seconds, on,
adaptive). Even participants who chose other combinations
also tended to prefer each of these options. In fact, for the
results shown in Figure 2, paired Wilcoxon signed-rank
tests with Bonferroni correction revealed that the medianof “5 seconds” was statistically higher than the other two
options for TimePreChorus at p < 0.01 6 . Similarly, “on”
for Crossfade and “adaptive” for TimeChorus were statistically higher than the other options at p < 0.01. In particular, it was not obvious that “5 seconds” was the most
preferred option for TimePreChorus, making this a useful,reusable insight for realizing chorus-playlist.
A music streaming service could offer the concept ofchorus-playlist by implementing a function that enables
users to listen to only choruses for all existing playlists on
the service. If a service provided this function, it would be
ideal to enable users to play playlists with arbitrary option
combinations, as we did, to reflect users’ preferences. If it
4 Note that the songs contained in the three playlists did not change;
only the options for playing them were changed.
5 It was not mandatory to listen to the playlists for all 18 option combinations. In fact, most participants reported their most preferred combination by narrowing down their preferences while switching options andlistening to the corresponding playlists.
6 Throughout this paper, ** (*) in a figure denotes a statistical difference at p < 0.01 (p < 0.05).

Proceedings of the 24th ISMIR Conference, Milan, Italy, November 5-9, 2023

Table 1. Preference distribution for option combinations.
TimePreChorus

CrossfadeOn

No playbackOffOn
5 seconds
OffOn
10 seconds
Off

TimeChorus
15 seconds
30 seconds
adaptive
15 seconds
30 seconds
adaptive
15 seconds
30 seconds
adaptive
15 seconds
30 seconds
adaptive
15 seconds
30 seconds
adaptive
15 seconds
30 seconds
adaptive# participnats
10 (4.67%)
3 (1.40%)38 (17.76%)
8 (3.74%)08 (3.74%)
26 (12.15%)
14 (6.54%)
49 (22.90%)
1 (0.47%)
1 (0.47%)
16 (7.48%)
3 (1.40%)
6 (2.80%)
22 (10.28%)
1 (0.47%)
1 (0.47%)
7 (3.27%)

Self-made playlists

Others' playlists
0

25

50

75

100

125

150

175

200

Number of participants
1: unwilling

3: somewhat unwilling

5: willing

2: not very willing

4: somewhat willing

6: very willing

Figure 3. Distribution of the willingness to listen to
playlists with the chorus-playlist function.

**
**
**

**

**

Figure 2. Preference distributions for each option.
is difficult to implement such a flexible function, chorusplaylist should be provided with the option combination (5seconds, on, adaptive), which should maximize the average user satisfaction according to this section’s results.
5. DEMAND FOR CHORUS-PLAYLISTWe described above how to implement a chorus-playlist
function. In this section, we investigate the demand forlistening to existing playlists with such a function.
5.1 Procedure
First, we showed the following description: “Suppose thata function to play only the choruses of the songs in aplaylist has become available on the music streaming service that you usually use. Please rate on a scale from 1(unwilling) to 6 (very willing) how much you would like to
use this function to listen to existing playlists that you created.” When the answer was “unwilling,” they were asked
to respond freely on why he/she did not want to use it.
Otherwise, when the answer was one of the remaining fiveitems, they were asked to respond freely with at least one
objective for listening to playlists with the function. We
did not set a cap on the number of responses.
Next, in a similar way, we asked the participants to indicate their willingness on a 6-point scale to use the function
to listen to existing playlists created by other users. According to their willingness, they were asked to provide
free responses as they did for the first question.
5.2 Results
Figure 3 shows the answer distribution. We refer toplaylists created by the participant and by other users “selfmade playlists” and “others’ playlists.” Only 11 and 3 participants answered “unwilling” for self-made playlists andothers’ playlists, respectively. The most popular reason for
unwillingness was “I believe that there is value in listening
to the entire song, including parts other than the chorus.”
On the other hand, because 75.2 % (161) and 82.7 % (177)participants for the two respective types of playlists answered “very willing,” “willing,” or “somewhat willing,”
we conclude that there is a sufficiently high demand forchorus-playlist.
We manually grouped the free responses on their objectives. When a response included multiple objectives corresponding to different groups, it was assigned to multiple
groups. Table 2 lists the top 10 objectives in terms of thegroup sizes for each of the two kinds of playlists. Each
number in parentheses indicates the number of participants
who gave that objective. Below, we discuss the results.
In the case of self-made playlists, the top three objectives could be achieved just by continuously listening to
the choruses of songs in a playlist. For example, the firstobjective was “boost a mood.” As self-made playlists usually contain songs that match the user’s own music preferences, the user’s mood would be boosted even when listening to playlists in the usual way [68]. Nevertheless, it isinteresting that the participants answered that they wanted
to further boost their mood by listening to only choruses.
It would be beneficial to recommend songs for a playlist
that are suitable for boosting a user’s mood when the user
listens to only the choruses in the playlist. For the second objective, the participants answered with various contexts such as “work” and “driving.” When listening to aplaylist with the chorus-playlist function in a specific context, there could be various reasons such as “increasing
concentration” and “relaxing.” It would be an interesting
future work to conduct a more detailed analysis of the contexts in which the conventional playlist listening approachor the chorus-playlist approach are preferred. As for the
fourth objective, it is known that people consider it valuable to listen to music with others and let others listen to
their favorite songs [81–84]. However, because it takes
much time for others to listen to all the songs in a playlist,
people may hesitate to introduce their favorite songs. Thus,
the participants answered that they wanted to efficiently introduce others such as friends or family to their favorite
songs when they listen to music together in person. Thatis, chorus-playlist could encourage people to interact withothers through music in the real world.
On the other hand, regarding the top six objectives
for others’ playlists, although those objectives could beachieved by conventional playlist listening, the participants
wanted to use the chorus-playlist function to achieve theobjectives more efficiently in a shorter time. In particular,
as seen from the first, second, fourth, and fifth objectives,
there is a strong demand for efficiently discovering and lis785

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Table 2. Top 10 free-response objectives for listening to playlists with the chorus-playlist function.
Rank
1
2
3
4
5
6
7
8
9
10

Self-made playlist
Boost a mood (79)
Listen to a playlist in a specific context (68)
Listen to a playlist within a limited time (46)
Recommend my favorite songs to others (36)
Explore desired songs (35)
Listen to many songs (23)Listen to various songs (20)Recall songs listened to in the past (18)Listen to only the choruses of my favorite songs (19)Sing songs in a playlist (12)tening to unfamiliar songs. Accordingly, if a music streaming service provides playlists consisting of hit songs of thepast week, songs by a specific artist, or songs in a specificgenre with the chorus-playlist function, many users would
likely listen to those playlists by using the function. This
would enable users to find more new favorite songs andhelp increase their music listening activity.
In summary, we have revealed that chorus-playlist cangenerate new interactions between people and music especially when they listen to self-made playlists. Moreover, as
discussed above, the results in Table 2 can provide useful
insights for both researchers and music streaming services.
6. IMPORTANT PROPERTIES FOR CREATINGPLAYLISTS IN CHORUS-PLAYLIST CONCEPT
6.1 Procedure
In section 5, we assumed application of the chorus-playlist
concept to existing playlists. However, by taking the concept a step further, a user could create a playlist on thepremise of continuous listening to only the choruses in theplaylist’s songs (for simplicity, we refer to creating sucha playlist as “creating a chorus-playlist”). Therefore, we
asked the participants how much they would like to create
chorus-playlists on the music streaming service that theyused regularly. They answered with their willingness on a
6-point scale from “unwilling” to “very willing.” We also
told the participants that they could also listen to each songfrom beginning to end through, not just the chorus.
When users create playlists, they consider certain properties such as the diversity of artists and the song order.
Hence, we wondered whether there are any differences regarding the importance of these properties when creating achorus-playlist as compared to creating a usual playlist. To
answer this question, we considered the following 11 properties derived from past studies [42, 70]. (1) SongHit: including songs with high popularity. (2) SongNew: including new songs in terms of the release dates. (3) ArtistSame:
including as many songs by the same artist as possible. (4)ArtistDiv: including songs by as many different artists as
possible. (5) GenreSame: including as many songs in thesame genre as possible. (6) GenreDiv: including songs
in as many different genres as possible. (7) MoodSame:
including as many songs with the same musical mood as
possible. (8) MoodDiv: including songs with as many different moods as possible. (9) SongOrder: the song orderin the playlist. (10) SongTop: the first song in the playlist.
(11) SongLast: the last song in the playlist.
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Others’ playlist
Find unfamiliar songs that suit my preference (109)
Listen to hit songs (48)
Learn other people’s music preferences (44)
Listen to songs by unfamiliar artists (37)
Listen to songs in unfamiliar genres (26)Preview a playlist (18)Listen to a playlist in a specific context (13)Refer to a playlist for creating my playlists (12)Listen to a playlist for a change of pace (9)Boost a mood (6)

0

25

50

75

100

125

150

175

200

Number of participants
1: unwilling

3: somewhat unwilling

5: willing

2: not very willing

4: somewhat willing

6: very willing

Figure 4. Willingness to create a chorus-playlist.
The participants who answered the first question withanything other than “unwilling” rated the importance ofeach property in creating a chorus-playlist and in creating ausual playlist on a 6-point scale from “not at all important”
to “very important.” The 11 properties were displayed in arandom order to each participant.
6.2 Results
Figure 4 shows that chorus-playlist creation has the potential to be a new way of enjoying music, because 75.7%(162) participants answered “very willing,” “willing,” or“somewhat willing,” while only 6.07% (13) participants
answered “unwilling.” Next, as shown in Figure 5, paired
Wilcoxon signed-rank tests indicated that statistical differences between the two playlist types were observed fornine properties 7 . Hence, we can say that people tended
to emphasize different properties when creating a chorusplaylist as compared to creating a usual playlist. Existing studies on song recommendation for playlists or
playlist generation have proposed various methods focusing on the song order in a playlist [54, 61, 64, 73]. However, for chorus-playlist, the SongOrder, FirstSong, and
LastSong properties were relatively less important. In
contrast, hit songs and new songs were more importantfor chorus-playlist. Furthermore, the results revealed theimportance of diversity in terms of artists, genres, andmoods for chorus-playlist. It has been reported that thediversities of artists and genres tend to be low in usual
playlists [70]; however, to support users creating chorusplaylists, it would be important to recommend songs to diversify such properties. These results thus open up newrecommendation approaches in the MIR community.
7. PLAYBACK METHOD COMPARISON
We have revealed a high demand to try chorus-playlist.
In this section, we investigate whether the chorus-playlist
playback method is preferred to other playback methods.
7.1 Procedure
For comparison, we used the following two types ofplaylists. (1) Head-playlist: a user continuously listens to
7 Figure 5 shows the results for the 201 participants besides the 13
participants who answered “unwilling.” The same statistical differenceswere obtained even with only the aforementioned 162 participants.

Proceedings of the 24th ISMIR Conference, Milan, Italy, November 5-9, 2023
]**

**

]**

**

]*
]**

Figure 6. Willingness for three playlist types.
]**

]*
]**
]**
]**

Figure 5. Property importance for playlist creation.
only the head sections of each song in a playlist. (2) 30secplaylist: a user continuously listens to only the parts after
the first 30 seconds of each song in a playlist. We adopted
30 seconds according to the preview samples on a musicstreaming service (Deezer) [85].
Similarly to the investigation described in section 4, we
had the participants listen to each type of playlist by selecting options for two choices. In the head-playlist case, thetwo choices were 1-second crossfade (options: “on” and“off”) and the playing time from the head of each song
(options: “15 seconds” and “30 seconds”). In the caseof 30sec-playlist, the two choices were 1-second crossfade (options: “on” and “off”) and the playing time after
the first 30 seconds of each song (options: “15 seconds”
and “30 seconds”). The participants listened to playlists
with each playback method by using their favorite option
combinations. Here, each participant was assigned threeplaylists containing the same songs as those in section 4.
Then, they were asked to rate their willingness to listen to
self-made playlists with each method on a 6-point scale.
7.2 Results
Figure 6 shows the results. Note that the chorus-playlist
results are repeated from “self-made playlists” in Figure 3.
Paired Wilcoxon signed-rank tests with Bonferroni correction revealed that the median for chorus-playlist was statistically higher than the medians for head-playlist and 30secplaylist. It thus became clear that it was not enough to simply play any part of the songs in a playlist continuously, butthat it was important for users to be able to play choruses
continuously.
8. DISCUSSION AND CONCLUSION
In this paper, we have studied the concept of chorusplaylist. The reusable insights obtained from our user survey can be summarized as follows.
• We showed that there is a high demand for chorusplaylist. When the participants listened to songs in aplaylist with chorus-playlist, they tended to prefer tolisten to 5 seconds before the chorus, add crossfade
transitions between songs, and listen to the chorus frombeginning to end. As discussed in section 7.2, it is more
important to play choruses continuously than to playother sections continuously.
• As seen in Table 2, the objectives for listening to music with chorus-playlist were largely different between
self-made playlists and others’ playlists. In particular,
certain objectives for self-made playlists were unexpected, in that people wanted to enjoy music in a newway with chorus-playlist for objectives such as boosting their mood. These results could provide guidelines
for researchers and services to consider new research
topics and activate user interaction, respectively.
• We revealed a high demand for creating a chorusplaylist. As in Figure 5, hit songs, new songs, and thediversities of artists, genres, and moods are more important when creating a chorus-playlist than when creating a usual playlist. These results also provide newviewpoints for studies on assisted playlist creation.
We acknowledge a limitation of this paper in that all
the participants in our user survey were Japanese. Because
peoples’ music preferences, listening behaviors, and music itself vary widely from country to country [86–90], not
all of the findings reported here can be generalized. Nevertheless, we believe that this study provides a worthwhile
contribution as a first step toward understanding the impactof the chorus-playlist concept. At the same time, this limitation indicates further possibilities such as investigating
the differences among countries and cultures. The publicly
available dataset of results from our user survey will enable
researchers to perform such comparisons.
Another limitation is that the participants did not experience chorus-playlist on the music streaming services theyusually used. However, because they answered the survey
after experiencing the chorus-playlist concept by listening
to the playlists that we provided, we think that they could
sufficiently imagine the situation of listening to self-madeplaylists and others’ playlists with the chorus-playlist approach. We currently provide the chorus-playlist function
in a music-related smartphone application (Vocacolle App)and web service (Kiite 8 ). In the future, we will investigate
the function’s usage in those more realistic environments.
Finally, although we considered only the first chorus ofa song (except when a song started with the chorus), thefinal chorus tends to be longer and contain heavier instrumentation than other choruses [1]. Therefore, it would also
be an interesting future work to investigate the impact ofdifferences between choruses in chorus-playlist listening.
Moreover, the concept of listening to only choruses canbe applied not only to playlists but also to other song lists
such as album track lists, which could further enrich anddiversify people’s music listening experience.
8 https://kiite.jp787

Proceedings of the 24th ISMIR Conference, Milan, Italy, November 5-9, 2023

9. ACKNOWLEDGMENTS
This work was supported in part by JST CREST Grant
Number JPMJCR20D4 and JSPS KAKENHI Grant Number JP21H04917, Japan.
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