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

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Book cover Culture and Computing (HCII 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13324))

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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.

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Author information

Authors and Affiliations

  1. Department of Communication, University of California Los Angeles, Los Angeles, CA, 90095, USA

    Yanru Jiang

  2. Department of Media and Communication, City University of Hong Kong, Kowloon, 999077, Hong Kong SAR

    Xin Jin

  3. Center for Intelligent Media and Communication Research, College of Literature and Journalism, Central South University, Changsha, 410017, Hunan, China

    Xin Jin

Authors
  1. Yanru Jiang
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  2. Xin Jin
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Correspondence to Yanru Jiang .

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Editors and Affiliations

  1. Eindhoven University of Technology, Eindhoven, The Netherlands

    Matthias Rauterberg

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Jiang, Y., Jin, X. (2022). Using k-Means Clustering to Classify Protest Songs Based on Conceptual and Descriptive Audio Features. In: Rauterberg, M. (eds) Culture and Computing. HCII 2022. Lecture Notes in Computer Science, vol 13324. Springer, Cham. https://doi.org/10.1007/978-3-031-05434-1_19

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  • DOI: https://doi.org/10.1007/978-3-031-05434-1_19

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-05433-4

  • Online ISBN: 978-3-031-05434-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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