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Robust handcrafted features for music genre classification

  • S.I.: Latin American Computational Intelligence
  • Published:
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Abstract

When it comes to offering song suggestions to a user, the music genre is one of the most used tags considered by music streaming services. Motivated by the growing number of songs available, automatic music genre classification systems have become a valuable tool for the creation of user-personalised playlists. Considering that feature engineering represents a major task to be addressed when one develops such systems, this work discusses the generation of new handcrafted features over songs, originally exploring high-order features’ moments combined with their derivatives. Additionally, this paper proposes a new wrapper-based selection procedure rigorously based on statistical tests to identify a subset of features that maximise the performance of these systems, irrespective of the classification approach adopted, named Robust Selector of Basis Feature Sets. Based on a synergistic combination of both strategies, a compact subset with 81 features is derived over the GTZAN Dataset. When compared with alternative solutions, this feature set boosted the classification accuracy in datasets containing a wide range of music genres, such as ISMIR2004, BALLROOM, HOMBURG, and FMA Datasets.

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Data availability

The datasets analysed are publicly available, as well the materials used in the implementation.

Code availability

The code can be requested from the authors.

Notes

  1. https://everynoise.com.

  2. https://newsroom.spotify.com/company-info/.

  3. https://scikit-learn.org/stable/modules/generated/sklearn.metrics.roc_auc_score.html.

  4. http://marsyas.info/downloads/datasets.html.

  5. https://www.upf.edu/web/mtg/ismir2004-genre.

  6. http://mtg.upf.edu/ismir2004/contest/tempoContest/node5.html.

  7. https://www-ai.cs.tu-dortmund.de/audio.html.

  8. https://github.com/mdeff/fma.

  9. https://scikit-learn.org.

  10. https://lightgbm.readthedocs.io.

  11. https://github.com/smazzanti/mrmr.

  12. https://scikit-posthocs.readthedocs.io.

  13. https://orange3.readthedocs.io/projects/orange-data-mining-library.

  14. https://matplotlib.org.

  15. https://librosa.org.

  16. https://docs.scipy.org/doc/scipy/index.html.

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Funding

This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES) Finance Code 001, CNPq, and FAPERJ.

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Author notes

  1. Victor Hugo da Silva Muniz and João Baptista de Oliveira e Souza Filho have contributed equally to this work.

Authors and Affiliations

  1. Electrical Engineering Program, Federal University of Rio de Janeiro, Av. Athos da Silveira Ramos, 149, Centro de Tecnologia, Bloco H, Sala 221, Rio de Janeiro, RJ, CEP 21.941-909, Brazil

    Victor Hugo da Silva Muniz & João Baptista de Oliveira e Souza Filho

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  1. Victor Hugo da Silva Muniz
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  2. João Baptista de Oliveira e Souza Filho
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The authors equally contributed to this work.

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Correspondence to Victor Hugo da Silva Muniz.

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da Silva Muniz, V.H., de Oliveira e Souza Filho, J.B. Robust handcrafted features for music genre classification. Neural Comput & Applic 35, 9335–9348 (2023). https://doi.org/10.1007/s00521-022-08069-5

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