Using k-Means Clustering to Classify Protest Songs Based on Conceptual and Descriptive Audio Features

Abstract

Protest music is a phenomenal and widely circulated form of protest art in social movements. Previous protest music research has extensively focused on lyrics while ignoring other musical features that also contribute to the role of protest music in social movements. This study fills a gap in previous research by converting 397 unstructured musical pieces into structured music features and proposing a k-means clustering analysis to categorize protest songs based on both high-level conceptual features collected from Spotify and low-level descriptive audio features extracted via Librosa. The Davies–Bouldin index, inertia curve, Silhouette curve, and Silhouette diagram were the main measurements used to compare model performance. An innovative threshold filtering approach (optimizer area) was used to label 128 protest songs. Through a bottom-up folksonomy approach to music classification, this study overcomes the limitations of traditional genre classification by introducing other high-level features (e.g., energy, danceability, instrumentalness) and their roles in determining protest music categories.

Full text

Using k-Means Clustering to Classify Protest
Songs Based on Conceptual and Descriptive
Audio Features
Yanru Jiang1(B)and Xin Jin2,3
1Department of Communication, University of California Los Angeles, Los Angeles,
CA 90095, USA
yanrujiang@g.ucla.edu
2Department of Media and Communication, City University of Hong Kong, Kowloon 999077,
Hong Kong SAR
xin.jin@my.cityu.edu.hk
3Center for Intelligent Media and Communication Research, College of Literature and
Journalism, Central South University, Changsha 410017, Hunan, China
Abstract. Protest music is a phenomenal and widely circulated form of protest art
in social movements. Previous protest music research has extensively focused on
lyrics while ignoring other musical features that also contribute to the role of protest
music in social movements. This study fills a gap in previousresearch by converting
397 unstructured musical pieces into structured music features and proposing
ak-means clustering analysis to categorize protest songs based on both high-
level conceptual features collected from Spotify and low-level descriptive audio
features extracted via Librosa. The Davies–Bouldin index, inertia curve, Silhouette
curve, and Silhouette diagram were the main measurements used to compare
model performance. An innovative threshold filtering approach (optimizer area)
was used to label 128 protest songs. Through a bottom-up folksonomy approach
to music classification, this study overcomes the limitations of traditional genre
classification by introducing other high-level features (e.g., energy, danceability,
instrumentalness) and their roles in determining protest music categories.
Keywords: Cultural computing ·Music classification ·Protest art
1 Introduction
2020 has been “the year of the social movement,” from the Hong Kong Anti-Extradition
Bill (Anti-ELAB) movement to Black Lives Matter (BLM). In all of these social move-
ments, music has become a phenomenal and widely circulated form of protest art [1].
Unlike most political songs, which serve the propagandistic function of promoting and
solidifying an official state perspective, protest songs challenge dominant ideologies
and powerful groups [2]. Previous protest music research has extensively focused on
lyrics, discussing their rhetorical effects, while ignoring other musical features—such
as melody, harmony, timbre, rhythm, and tempo—that also contribute to the role of
protest music in social movements [3].
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2022
M. Rauterberg (Ed.): HCII 2022, LNCS 13324, pp. 291–304, 2022.
https://doi.org/10.1007/978-3-031-05434-1_19

292 Y. Jiang and X. Jin
This study bridges the gap in existing protest music research by converting unstruc-
tured musical pieces to structured musical features and proposing a k-means clustering
analysis to categorize protest songs based on both high-level conceptual and low-level
descriptive audio features extracted using the Spotify API and the Librosa music analy-
sis tool. The Davies–Bouldin (DB) index, inertia curve, Silhouette curve, and Silhouette
diagram were the main measurements used to compare model performance of the k-
means algorithms. An innovative threshold filtering approach called optimizer area was
also employed to generate labels for 128 protest songs based on the clustering analysis.
2 Literature Review
2.1 Protest Music
Mondak [4] defines protest music as a form of political persuasion, as one of its functions
is “the attempt to generate outside support for the critical views it expresses” (p. 25).
Emphasizing lyrics, Denisoff [5] identified two functions of protest songs—the magnetic
and the rhetorical—viewing the music as secondary to the lyrics in realizing these func-
tions. Magnetic songs encourage and recruit non-participants to join the social movement
and reinforce existing participants’ commitment through the messages of group solidar-
ity and identity. Rhetorical songs, rather than appealing to group identity and promoting
a large movement, describe the dissent, repression, and struggles faced by individuals
in a group. In his research on the lyrics of protest music after the 2000s, Cort [6] reflects
on the progression of music’s roles in social movements and extends Denisoff’s [5]
twofold classification. After advocacy for issues like climate change began to emerge
after the millennium, group solidarity and individual adversity were no longer the sole
messages delivered by protest songs. Rather, as identified by Cort [6], protest music
in the modern world progressively came to include the roles of “challenging a power
structure, empowering the listener to overcome adversity, communicating a message to
solidify members of a social movement, and encouraging societal change” (p. 5).
As research on the lyrics of protest music began to reach a consistent and compre-
hensive conclusion regarding the role of protest songs in social movements, Eyerman
and Jamison [7] challenged this unilateral emphasis on lyrics for studying protest songs.
Responding to Denisoff’s [5] study, which overemphasized lyrics, the authors argued
that the music itself also contributes to protest songs’ magnetic and rhetorical functions.
Specifically, the structure of protest songs, which are “buil[t] around known and catchy
melodies, repeating verses, [and] simple chords” (p. 43), conveys an explicit persuasive
effect. Additionally, Eyerman and Jamison [7] focus on a cultural perspective: since
protest music’s contextual messages and status as a bearer of musical traditions could be
reflected in its familiar tunes and genres, which appeal to certain groups and cultures, the
cultural elements of protest music link the past, present, and future of the revolutionary
groups. Preexisting African American songs provided new kinds of emancipatory and
visionary messages for the U.S. civil rights movement in the late 1950s and early 1960s.
White power music—including country, folk, Viking, black metal, and hard rock, which
developed in the Western world—is commonly found in neo-fascist movements [8].
At the same time, numerous cognition and psychology studies have investigated
the impact of audio features on audiences’ perceptions. Bainbridge et al. [9] found

Using k-Means Clustering to Classify Protest Songs 293
that both infants and adults demonstrated psychological responses to unfamiliar foreign
songs, indicating that audio features alone could affect perception without any additional
context. Given that most previous protest music studies have been conducted from lyrics-
focused, cultural, generic, and qualitative perspectives, this study selects audio-level
music features, such as arousal, melody, rhythm, and timbre, to empirically analyze the
following research questions [10,11]:
RQ1: How should protest songs be classified based on high-level features and low-level
descriptors?
RQ2: Which combination of features produces the optimal results for classifying protest
songs?
2.2 Music Categorization
Though most previous studies on music classification have focused on genre classifi-
cation, Scaringella and Zoia [3] claim that most generic approaches remain fuzzy and
use loosely defined generic concepts. Possible issues arising from genre classification
include the simplification of artists’ discourse, unstandardized merging of multiple gen-
res, the need to split one genre into various subgenres, and the overall difficulty in
agreeing on a universal taxonomy for music classification. This study challenges the tra-
ditional generic approach by incorporating high-level musical features to identify how
different sets of features could stimulate different emotional reactions from audiences.
Scaringella and Zoia [3] proposed an innovative solution for classifying music
termed “folksonomy,” a bottom-up approach wherein users generate classifiers through
an interactive mutual agreement. This study recruited two content coders and adopted a
folksonomy-based method for labeling clusters of protest songs.
3 Conceptual Framework
Early research on music classification used static clustering methods and has since con-
tinually advanced, especially after the introduction of deep learning and neural networks
[12]. The primary focuses in this area have two components: (i) feature selection and
extraction, and (ii) classifier designs [13]. The majority of music classification research
explores genre classification using the GTZAN dataset created by Tzanetakis and Cook
[14] which contains 10 genre labels: blues, classical, country, disco, hip hop, jazz, metal,
pop, reggae, and rock [15]. Audio feature extraction and music clustering have also
shown promise in music tagging, recommendation, and classification with regard to pre-
viously undifferentiable music categories, such as traditional music and song emotion
recognition [16,17].
3.1 Audio Feature Extraction
Feature extraction allows researchers to convert unstructured musical pieces into struc-
tured data input by using high- and low-level features to capture the differences between
pieces of music.

294 Y. Jiang and X. Jin
High-Level Features. High-level statistical acoustic features are generally found to have
better classifying performance than low-level features [18]. Lidy and Rauber [19]
conducted research on psychoacoustic transformation in the context of music genre
classification by measuring audiences’ psychological responses and connecting those
responses to certain high-level audio features of the music. Additionally, high-level fea-
tures demonstrate better performance in emotion recognition compared with low-level
features [19].
Rodà et al. [20] identified valence and arousal as the most frequently researched
dimensions among high-level music features. According to Dillman Carpentier and
Potter [10], arousal, level of hedonic intensity, and energetic value are commonly studied
along with valence (i.e., the negative or positive value of the hedonic level). While the
valence–arousal plane has been dominant in studies of musical features, Rodà et al. [20]
recommend including other features, such as loudness, roughness, and spectral features,
to measure the emotional power of the music.
Low-Level Features. Most of the research on genre classification is conducted on low-
level descriptors, mainly focusing on features in music signal processing that simulate
human auditory perception, including rhythmic, temporal, cepstral, spectral, and per-
ceptual audio descriptors [21]. Scaringella and Zoia [3] found that low-level descriptors
in audio signals could capture melodic and harmonic elements of music more robustly
than high-level features.
3.2 Music Classifier Design
With the development of greater computing power and advanced deep learning algo-
rithms, the research focus of music classifier design has shifted from more static, classic
models—such as logistic regression, k-nearest neighbors, and support vector machines
(SVMs)—to more novel, dynamic approaches, including convolutional neural networks
(CNNs) and recurrent neural networks (RNNs). Tzanetakis and Cook [14] pioneered
the genre classification field with their use of a mixed-methods design consisting of k-
nearest neighbors and Gaussian mixture models to introduce a comprehensive clustering
pipeline. The two authors further standardized the feature categories for music clustering
(rhythm, pitch, and temporal structure) for future studies. Li et al. [22] proposed content-
based genre clustering, emphasizing the multiclass attributes enabled by the histogram
classification approach. Xu et al. [23] applied an SVM for music classification tasks,
while Fu et al. [24] challenged the SVM design by combining a naïve Bayes classifier
with an SVM to produce an NBSVM algorithm.
Regarding dynamic music classifiers, neural networks have shown success in reflect-
ing the temporal features in a sequence of music timesteps. Vishnupriya and Meenakshi
[25] proposed a CNN architecture and implemented it on Mel spectrum and MFCC
feature vectors for each song. Grzegorz [26] trained a CNN model for image recognition
and conducted transfer learning by reusing the bottom levels of that trained model for
genre recognition. Considering music as a sequence of sounds with embedded temporal
features, Ghosal and Sarkar [12] designed a model consisting of four layers of CNNs
with a final long short-term memory (LSTM) RNN layer to construct a new clustering

Using k-Means Clustering to Classify Protest Songs 295
augmented learning method for genre classification. With the convolutional layer and
LSTM autoencoders in the last layer, the authors were able to capture both spatial and
temporal features from the music signal.
Among all classifier designs, k-means clustering is one of the most commonly used
and most straightforward algorithms for clustering analysis. Jondya and Iswanto [27]
extracted the most relevant audio features through principal components analysis and
used k-means clustering to categorize Indonesian traditional music based on 36 low-level
descriptors. Kim et al. [17] innovatively applied k-means clustering to a music recom-
mendation system and introduced a dynamic clustering algorithm capable of reflecting
changes in users’ playlists.
4 Methods
4.1 Data Collection
Several steps were involved in the data collection process. First, I used the Wikipedia
protest songs category to generate a list of protest songs from the 19th through 21st
centuries. Second, I matched Wikipedia songs with the Spotify music database to col-
lect Spotify music features and 30-s MP3 music demos to be used for Librosa feature
extraction in the next step.
Wikipedia List. As there lacked an existing list comprehensively covering global protest
songs, I ensured the representativeness and authority of the data by confirming our list
synthetically based on qualitative literature on typical protest songs (e.g., Dear Mr.
President), lists published by authoritative music brands (e.g., RadioX), and the Protest
Songs Wikipedia Typology page, which is under the authority of the Library of Congress.
I ultimately produced a list of 457 songs with music production information, such as
language, release date, genre, and composer.
Spotify. I next used the Spotify API to match songs in the Wikipedia list with the Spotify
database according to title, artists, album, and release year. Because the official Spotify
Web API is based on the REST (Representational State Transfer) principle, which is not
directly compatible with the Python language, Spotipy—a lightweight Python library
for the Spotify Web API—was used instead to query music features from the Spotify
database. Realizing that the search results in Spotify may not automatically refer to the
same songs in the Wikipedia list, I filtered out irrelevant songs when the number of songs
in the search results was greater than 50.
Content Coding. Two coders specializing in social movement studies conducted content
coding on 457 protest songs by manually verifying whether the search result in the
Spotify database matched the music indicated on the Wikipedia list. The MP3 music
demos provided by Spotify were used to confirm and select the corresponding song
version, while the two coders also used the YouTube video search results to cross-check
the accuracy of the Spotify music selection. After a systematic data cleaning procedure,
397 songs that could be matched with the Spotify API were retained for feature extraction.

296 Y. Jiang and X. Jin
4.2 Feature Extraction
Feature extraction and selection were performed for both the high-level conceptual fea-
tures provided by the Spotify API and the low-level descriptive features extracted from
the Librosa Python library. The study conducted k-means clustering on high-level and
low-level features, respectively, to compare the performance of both sets of features.
Considering that differences in feature values may substantially distort the mapping and
clustering process, I normalized both high-level and low-level features from 0 to 1.
High-Level Features: Spotify. To operationalize the conceptual-level audio features, the
Spotify API was used to extract the technological-level audio features of each protest
song. The Echo Nest, a music intelligence and data platform, first introduced audio fea-
ture extraction algorithms to the music world; its algorithm has been integrated with the
Spotify Audio Feature API since Spotify acquired The Echo Nest in 2014 [28] According
to the Object Index in the Spotify API documentation, 10 audio features (acousticness,
danceability, energy, instrumentalness, key, liveness, loudness, speechiness, tempo, and
valence) are provided by the Spotify API. A detailed description of each audio feature
is provided in the Appendix.
For each piece of music, I selected eight features—danceability, energy, speechiness,
acousticness, instrumentalness, liveness, valence, and tempo—from the Spotify API for
clustering analysis based on their relevance to the music being categorized. Loudness
was excluded because its presence would not substantially affect audiences’ emotional
perceptions of the protest music. Loudness can fluctuate based on the user’s volume
setting, while liveness can also easily vary based on the recording environment during
the music production. Therefore, liveness has only a moderate effect on the clustering
results (Fig. 1).
Fig. 1. Distribution of high-level audio features
Low-Level Descriptors: Librosa. Librosa, first introduced by McFee et al. [29], is a
commonly used Python package for music and audio signal processing. Unlike the Spo-
tify API, which extracts high-level audio features, Librosa extracts low-level descriptive

Using k-Means Clustering to Classify Protest Songs 297
features from audio clips, some of which are not readily perceptible to audiences, such as
spectral features. Librosa is usually considered comprehensive for music studies because
it provides all relevant parameters for audio analysis [29].
In total, 320 music files downloaded using the Spotify API were used for Librosa
feature extraction. This number is smaller than the number of songs selected for Spotify
feature extraction because 77 songs were excluded due to their lack of MP3 demos. To
enable Librosa to process the music files, I converted the 320 MP3 files to WAV format
using an online converter tool on a pay-as-you-go basis. Librosa reads WAV files into a
one-dimensional NumPy array (denoted as y) and calculates the song duration in seconds
based on the sampling rate:
Duration Seconds =float(len(y))
sampling rate (1)
Spectral features, defined as “the distribution of energy over a set of frequencies”
[29], are the dominant features in Librosa feature extraction and in digital signal process-
ing. Among all spectral features, the Mel spectrogram frequency scale (melspectrogram)
and its derivatives (mfcc,rms,poly_features) are frequently used to capture the timbral
element of music. Specifically, the spectrogram represents the music’s time and fre-
quency information and has been widely used in recently developed music classification
models [12].
Besides capturing timbral elements, “pitch class representations (chroma_stft,
chroma_cqt, chroma_cens, tonnetz) are often used to encode harmony while suppress-
ing variations in octave height, loudness, or timbre” [29]. Additionally, Librosa provides
spectral statistic representations for music, including spectral centroid, bandwidth, roll-
off, and contrast. Zero-crossing rate, a temporal feature measuring “signal changes from
positive to zero to negative or from negative to zero to positive” was also added to the
feature selection [3,30]. One rhythm feature, Fourier tempogram, was also included for
the purpose of clustering Librosa audio features.
4.3 Clustering
Iusedk-means clustering to categorize music computationally, ultimately arriving at 10
clusters for high-level features and seven clusters for low-level descriptors based on the
DB index. The clustering results of the perceptual high-level feature set were further
verified and adjusted by human coders through a folksonomy classification approach.
Six subcategories and three parent categories were ultimately identified for this feature
set.
Several metrics, including the inertia curve, Silhouette curve, Silhouette diagram,
and DB index, were used to compare and measure model performance. After realizing
that the inertia curve, Silhouette curve, and Silhouette diagram could not easily identify
the optimal number of music clusters for both datasets in the study, I selected models
based on which had the lowest DB index.

298 Y. Jiang and X. Jin
4.4 Music Category Optimization
Once I determined the number of clusters using the DB index, I defined an optimizer
area to select protest songs that were more likely to be aligned with the corresponding
clusters and filter out the less relevant ones.
In the optimization step, I calculated the mean value of eight features in each cluster
and selected them as baselines by adding or subtracting the standard deviation with
various multipliers to obtain the optimizer area. Songs with feature values outside this
area were excluded. Based on the optimized clusters, whose category attributes are more
aggregate and salient, I ultimately identified six categories of protest music by dropping
noise clusters and merging similar ones (Table 2; dataset: https://bit.ly/3vg6n6c).
Optimizer Area =Meanfeature ±Multiplier ×Standard Deviationfeature (2)
Table 1. Comparison of four measurements used to evaluate clustering model performance
High-level (Spotify) Low-level (Librosa)
Davies-
Bouldin
The DB index was smallest (DB =
1.25) when the number of clusters was
set to 10
The DB index was smallest (DB =
1.48) when the number of clusters
was set to 7
Inertia
curve
Silhouette
curve
Silhouette
diagram

Using k-Means Clustering to Classify Protest Songs 299
5 Results
5.1 Performance Measurement
For both high-level and low-level features, the inertia curve, Silhouette curve, and Sil-
houette diagram were not able to produce a distinct pattern in the clustering analysis
(see Table 1). The DB index was thus selected for comparing model performance. As
the DB index was used to measure similarity across clusters, a lower DB index indicates
a better clustering result.
High-Level Features. The DB index was smallest when the number of clusters was
set to 10 (k=10) for the high-level feature set. According to the inertia curve and
Silhouette curve above, none of these graphs shows a distinct elbow (i.e., turning point)
for identifying the best number of clusters for the high-level feature set. The Silhouette
diagram indicates the same result as the DB index: protest songs are less likely to be
misclassified to the wrong clusters when kequals 10 for high-level features.
Fig. 2. Heat map of cluster results of high-level (left) and low-level feature set (right)
The heat map (see Fig. 2) reveals the similarities and differences in feature values
across the 10 clusters. Specifically, KM4 and KM8 share a similar distribution of music
features, and clusters tend to have distinctive values in the energy and valence features.
Using the optimizer area approach, I identified 128 protest songs that were more
likely to be aligned with the feature pattern in their corresponding clusters (Fig. 3).
Low-Level Descriptors. Neither the inertia curve nor the Silhouette curve graph below
shows a distinct elbow for identifying the best number of clusters for the low-level feature
dataset. The Silhouette diagram indicates the same result as the DB index: for low-level
features, protest songs are less likely to be misclassified to the wrong clusters when k
equals 7.
The heat map for the cluster results of the low-level feature set reveals that KM3 and
KM4 have similar distributions of music features, and clusters tend to have distinctive
values in the tempogram and MFCC features.
The distribution of the low-level features across the seven clusters reveals that
KM0 and KM6 have similar distributions of music features, while most clusters are
substantially different from the remaining clusters (Fig. 4).

300 Y. Jiang and X. Jin
Fig. 3. Distribution of high-level music features by cluster.Gray curves indicate music with
feature values outside the optimizer area.
5.2 Clustering and Annotating Results
Through an unsupervised clustering analysis, this study makes a pioneering classification
of protest songs into six subcategories within three overarching categories by emphasiz-
ing audio feature variation. Category A shows that the salient attributes of rock music are
its typically high energy and low acousticness. Category B is associated with a high vol-
ume of vocal sounds (high acousticness). Category C, representing the attributes of folk
and country music, maintains relatively balanced values of energy and acousticness.
Moreover, for each subcategory, some other features were also significant in catego-
rizing the music. For example, high danceability provides songs in subcategory KM0
with strong beats; thereby, our study challenges the traditional generic categorization of
protest songs (Table 2).

Using k-Means Clustering to Classify Protest Songs 301
Fig. 4. Distribution of low-level music features by cluster
Table 2. Final music categories with quantities, descriptions, and Spotify links.
Category NDescription Representative music
A-km0 21 Dance rock, post-punk, dance music https://spoti.fi/3pRZUdH
B-km1 21 Highly vocal songs https://spoti.fi/3bBpgaQ
A-km2 21 Hard rock with high tempo https://spoti.fi/3dIOWF7
C-km3/5 32 Folk and country music with low tempo https://spoti.fi/3aRFrl6
C-km6 13 Folk and country music with high tempo https://spoti.fi/3sqPTWt
A-km7 20 Soft rock https://bit.ly/3uzmVWk
6 Conclusion
This study fills a gap in previous protest music research, which has historically ignored
audio features when classifying music. Specifically, this research converted 397 unstruc-
tured musical pieces into structured music features and proposed a k-means clustering

302 Y. Jiang and X. Jin
analysis to categorize protest songs based on both high-level conceptual features col-
lected from Spotify and low-level descriptive audio features extracted via Librosa. The
DB index was the main metric used to compare model performance. An innovative
threshold filtering approach (optimizer area) was used to label 128 protest songs. Through
a bottom-up folksonomy approach to music classification, this study overcomes the lim-
itations of traditional genre classification by introducing other high-level features (e.g.,
energy, danceability, instrumentalness) and their roles in determining music clusters.
Due to the lack of perceivability of Librosa features, this study did not manually
label the final categorical results for the low-level feature set. Future research could
conduct a time-series analysis following the clustering method proposed in this study to
investigate how protest music categories shift across centuries.
6.1 Limitations and Future Work
The research has several limitations. First, the Wikipedia typology might not present a
comprehensive list of protest songs. Several songs also were dropped due to a lack of
matching results in the Spotify database. However, covering almost 400 protest songs
from the 19th through 21st centuries for music categorization is already a record-breaking
feat in protest music studies [6]. Second, due to the imperceptibility of low-level music
descriptors, this study could not manually verify the clustering results produced by the
Librosa feature set. In future research, this issue could be overcome by cross-checking
with the categorizing results of Spotify features or other state-of-the-art approaches in
the music classification field. Lastly, the Librosa feature set contains several audio fea-
tures that share similar attributes and are highly correlated, such as Mel spectrogram and
MFCC, which might lead to collinearity in the feature selection process. It is recom-
mended to verify multicollinearity within spectral and rhythm features before clustering
music based on low-level descriptors.
7 Data and Code Availability
Data and code: https://github.com/JoyceJiang73/Protest_Music_Clustering.
References
1. Green Jr., D.F.: Views from the bricks: notes on reading and protest. CLA J. 63, 169–173
(2020). https://doi.org/10.34042/claj.63.2.0169
2. Philpott, S.: Of country and country: twang and trauma in Australian Indigenous popular
music. Politik 23, 94–98 (2020). https://doi.org/10.7146/politik.v23i1.120312
3. Scaringella, N., Zoia, G., Mlynek, D.: Automatic genre classification of music content. IEEE
Signal Process. Mag. 23, 133–141 (2006). https://doi.org/10.1109/MSP.2006.1598089
4. Mondak, J.J.: Protest music as political persuasion (1988). https://doi.org/10.1080/030077
68808591322
5. Denisoff, R.S.: Songs of persuasion: a sociological analysis of urban propaganda songs. J.
Am. Folk. 79, 581–589 (1966). https://doi.org/10.2307/538223
6. Quirk Cort, M.E.: The Power of Lyrical Protest: Examining the Rhetorical Function of Protest
Songs in the 2000s (2013)

Using k-Means Clustering to Classify Protest Songs 303
7. Eyerman, R., Jamison, A.: Taking traditions seriously. In: Music and Social Movements:
Mobilizing Traditions in the Twentieth Century, pp. 26–47. Cambridge University Press
(1998). https://doi.org/10.2307/767983
8. Eyerman, R.: Music in movement: cultural politics and old and new social movements. Qual.
Sociol. 25, 443–458 (2002). https://doi.org/10.1023/A:1016042215533
9. Bainbridge, C.M., et al.: Infants relax in response to unfamiliar foreign lullabies. Nat. Hum.
Behav. 5, 256–264 (2021). https://doi.org/10.1038/s41562-020-00963-z
10. Dillman Carpentier, F.R., Potter, R.F.: Effects of music on physiological arousal: explorations
into tempo and genre (2007). https://doi.org/10.1080/15213260701533045
11. Rosenthal, R.: Serving the movement: the role(s) of music. Pop. Music Soc. 25, 11–24 (2001)
12. Ghosal, S.S., Sarkar, I.: Novel approach to music genre classification using clustering
augmented learning method (CALM). In: CEUR Workshop Proceedings, vol. 2600 (2020)
13. Tsai, W.H., Bao, D.F.: Clustering music recordings based on genres. J. Inf. Sci. Eng. 26,
2059–2074 (2010)
14. Tzanetakis, G., Cook, P.: Musical genre classification of audio signals. In: IEEE Transactions
on Speech and Audio Processing, pp. 293–302. IEEE (2002)
15. Cheng, Y.H., Chang, P.C., Kuo, C.N.: Convolutional neural networksapproach for music genre
classification. In: Proceedings of 2020 International Symposium on Computer, Consumer and
Control, IS3C 2020, pp. 399–403 (2020). https://doi.org/10.1109/IS3C50286.2020.00109
16. Choi, K., Fazekas, G., Sandler, M., Cho, K.: Convolutional recurrent neural networks for
music classification. In: ICASSP, IEEE International Conference on Acoustics, Speech and
Signal Processing Proceedings, pp. 2392–2396 (2017). https://doi.org/10.1109/ICASSP.2017.
7952585
17. Kim, D.M., Kim, K.S., Park, K.H., Lee, J.H., Lee, K.M.: A music recommendation system
with a dynamic K-means clustering algorithm. In: Proceedings of the 6th International Con-
ference on Machine Learning and Applications, ICMLA 2007, pp. 399–403 (2007). https://
doi.org/10.1109/ICMLA.2007.97
18. Atmaja, B.T., Akagi, M.: On the differences between song and speech emotion recognition:
effect of feature sets, feature types, and classifiers. In: IEEE Region 10 Annual International
Conference Proceedings/TENCON, November 2020, pp. 968–972 (2020). https://doi.org/10.
1109/TENCON50793.2020.9293852
19. Lidy, T., Rauber, A.: Evaluation of feature extractors and psycho-acoustic transformations
for music genre classification. In: ISMIR 2005, 6th International Conference on Music
Information Retrieval, pp. 34–41 (2005)
20. Rodà, A., Canazza, S., De Poli, G.: Clustering affective qualities of classical music: beyond
the valence-arousal plane. IEEE Trans. Affect. Comput. 5, 364–376 (2014). https://doi.org/
10.1109/TAFFC.2014.2343222
21. Blaszke, M., Koszewski, D.: Determination of low-level audio descriptors of a musical instru-
ment sound using neural network. In: Signal Processing: Algorithms, Architectures, Arrange-
ments, and Applications Proceedings, SPA, September 2020, pp. 138–141 (2020). https://doi.
org/10.23919/spa50552.2020.9241264
22. Li, T., Ogihara, M., Li, Q.: A comparative study on content-based music genre classification.
In: Proceedings of the 26th Annual International ACM SIGIR Conference on Research and
Development in Informaion Retrieval, pp. 282–289, Toronto, Canada (2003). https://doi.org/
10.1109/ISSPA.2003.1224828
23. Xu, C., Maddage, N.C., Shao, X., Cao, F., Tian, Q.: Musical genre classification using support
vector machines. In: 2003 IEEE International Conference on Acoustics, Speech, and Signal
Processing, pp. 429–432 (2003)
24. Fu, Z., Lu, G., Ting, K.M., Zhang, D.: Learning Naive Bayes classifiers for music classifi-
cation and retrieval. In: Proceedings of the International Conference on Pattern Recognition,
pp. 4589–4592 (2010). https://doi.org/10.1109/ICPR.2010.1121

304 Y. Jiang and X. Jin
25. Vishnupriya, S., Meenakshi, K.: Automatic music genre classification using convolution neu-
ral network. In: 2018 International Conference on Computer Communication and Informatics,
ICCCI 2018, pp. 4–7 (2018). https://doi.org/10.1109/ICCCI.2018.8441340
26. Gwardys, G., Grzywczak, D.: Deep image features in music information retrieval. Int. J.
Electron. Telecommun. 60, 321–326 (2014). https://doi.org/10.2478/eletel-2014-0042
27. Jondya, A.G., Iswanto, B.H.: Indonesian’s traditional music clustering based on audio fea-
tures. Procedia Comput. Sci. 116, 174–181 (2017). https://doi.org/10.1016/j.procs.2017.
10.019
28. Skidén, P.: New Endpoints: Audio Features, Recommendations and User Taste. https://develo
per.spotify.com/community/news/2016/03/29/audio-features-recommendations-user-taste/
29. McFee, B., et al.: librosa: audio and music signal analysis in Python. In: Proceedings of the
14th Python in Science Conference, pp. 18–24 (2015). https://doi.org/10.25080/majora-7b9
8e3ed-003
30. Stürmer, S., Simon, B.: The role of collective identification in social movement participation:
a panel study in the context of the German gay movement. Personal. Soc. Psychol. Bull. 30,
263–277 (2004). https://doi.org/10.1177/0146167203256690