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DBTMPE: Deep Bidirectional Transformers-Based Masked Predictive Encoder Approach for Music Genre Classification

Author

Listed:
  • Lvyang Qiu

    (Department of Multimedia Engineering, Dongguk University–Seoul, Seoul 04620, Korea)

  • Shuyu Li

    (Department of Multimedia Engineering, Dongguk University–Seoul, Seoul 04620, Korea)

  • Yunsick Sung

    (Department of Multimedia Engineering, Dongguk University–Seoul, Seoul 04620, Korea)

Abstract

Music is a type of time-series data. As the size of the data increases, it is a challenge to build robust music genre classification systems from massive amounts of music data. Robust systems require large amounts of labeled music data, which necessitates time- and labor-intensive data-labeling efforts and expert knowledge. This paper proposes a musical instrument digital interface (MIDI) preprocessing method, Pitch to Vector (Pitch2vec), and a deep bidirectional transformers-based masked predictive encoder (MPE) method for music genre classification. The MIDI files are considered as input. MIDI files are converted to the vector sequence by Pitch2vec before being input into the MPE. By unsupervised learning, the MPE based on deep bidirectional transformers is designed to extract bidirectional representations automatically, which are musicological insight. In contrast to other deep-learning models, such as recurrent neural network (RNN)-based models, the MPE method enables parallelization over time-steps, leading to faster training. To evaluate the performance of the proposed method, experiments were conducted on the Lakh MIDI music dataset. During MPE training, approximately 400,000 MIDI segments were utilized for the MPE, for which the recovery accuracy rate reached 97%. In the music genre classification task, the accuracy rate and other indicators of the proposed method were more than 94%. The experimental results indicate that the proposed method improves classification performance compared with state-of-the-art models.

Suggested Citation

  • 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.
  • Handle: RePEc:gam:jmathe:v:9:y:2021:i:5:p:530-:d:509902
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    References listed on IDEAS

    as
    1. Shuyu Li & Sejun Jang & Yunsick Sung, 2019. "Automatic Melody Composition Using Enhanced GAN," Mathematics, MDPI, vol. 7(10), pages 1-13, September.
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    Cited by:

    1. Shuyu Li & Yunsick Sung, 2023. "MRBERT: Pre-Training of Melody and Rhythm for Automatic Music Generation," Mathematics, MDPI, vol. 11(4), pages 1-14, February.
    2. Yihang Zhang & Yunsick Sung, 2023. "Traffic Accident Detection Method Using Trajectory Tracking and Influence Maps," Mathematics, MDPI, vol. 11(7), pages 1-14, April.
    3. Yihang Zhang & Yunsick Sung, 2023. "Traffic Accident Detection Using Background Subtraction and CNN Encoder–Transformer Decoder in Video Frames," Mathematics, MDPI, vol. 11(13), pages 1-15, June.
    4. 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.
    5. Zhe Jiang & Shuyu Li & Yunsick Sung, 2022. "Enhanced Evaluation Method of Musical Instrument Digital Interface Data based on Random Masking and Seq2Seq Model," Mathematics, MDPI, vol. 10(15), pages 1-17, August.

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