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Machine Learning for Music Genre Classification Using Visual Mel Spectrum

Author

Listed:
  • Yu-Huei Cheng

    (Department of Information and Communication Engineering, Chaoyang University of Technology, Taichung 413, Taiwan)

  • Che-Nan Kuo

    (Department of Artificial Intelligence, CTBC Financial Management College, Tainan 709, Taiwan)

Abstract

Music is the most convenient and easy-to-use stress release tool in modern times. Many studies have shown that listening to appropriate music can release stress. However, since it is getting easier to make music, people only need to make it on the computer and upload it to streaming media such as Youtube, Spotify, or Beatport at any time, which makes it very infeasible to search a huge music database for music of a specific genre. In order to effectively search for specific types of music, we propose a novel method based on the visual Mel spectrum for music genre classification, and apply YOLOv4 as our neural network architecture. mAP was used as the scoring criterion of music genre classification in this study. After ten experiments, we obtained a highest mAP of 99.26%, and the average mAP was 97.93%.

Suggested Citation

  • Yu-Huei Cheng & Che-Nan Kuo, 2022. "Machine Learning for Music Genre Classification Using Visual Mel Spectrum," Mathematics, MDPI, vol. 10(23), pages 1-19, November.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:23:p:4427-:d:982470
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    References listed on IDEAS

    as
    1. Qi He & Naeem Jan, 2022. "A Music Genre Classification Method Based on Deep Learning," Mathematical Problems in Engineering, Hindawi, vol. 2022, pages 1-9, March.
    2. Lvyang Qiu & Shuyu Li & Yunsick Sung, 2021. "DBTMPE: Deep Bidirectional Transformers-Based Masked Predictive Encoder Approach for Music Genre Classification," Mathematics, MDPI, vol. 9(5), pages 1-17, March.
    Full references (including those not matched with items on IDEAS)

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