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Enhanced Evaluation Method of Musical Instrument Digital Interface Data based on Random Masking and Seq2Seq Model

Author

Listed:
  • Zhe Jiang

    (Department of Autonomous Things Intelligence, Graduate School, Dongguk University–Seoul, Seoul 04620, Korea)

  • Shuyu Li

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

  • Yunsick Sung

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

Abstract

With developments in artificial intelligence (AI), it is possible for novel applications to utilize deep learning to compose music by the format of musical instrument digital interface (MIDI) even without any knowledge of musical theory. The composed music is generally evaluated by human-based Turing test, which is a subjective approach and does not provide any quantitative criteria. Therefore, objective evaluation approaches with many general descriptive parameters are applied to the evaluation of MIDI data while considering MIDI features such as pitch distances, chord rates, tone spans, drum patterns, etc. However, setting several general descriptive parameters manually on large datasets is difficult and has considerable generalization limitations. In this paper, an enhanced evaluation method based on random masking and sequence-to-sequence (Seq2Seq) model is proposed to evaluate MIDI data. An experiment was conducted on real MIDI data, generated MIDI data, and random MIDI data. The bilingual evaluation understudy (BLEU) is a common MIDI data evaluation approach and is used here to evaluate the performance of the proposed method in a comparative study. In the proposed method, the ratio of the average evaluation score of the generated MIDI data to that of the real MIDI data was 31%, while that of BLEU was 79%. The lesser the ratio, the greater the difference between the real MIDI data and generated MIDI data. This implies that the proposed method quantified the gap while accurately identifying real and generated MIDI data.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:15:p:2747-:d:879242
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    References listed on IDEAS

    as
    1. Lvyang Qiu & Shuyu Li & Yunsick Sung, 2021. "3D-DCDAE: Unsupervised Music Latent Representations Learning Method Based on a Deep 3D Convolutional Denoising Autoencoder for Music Genre Classification," Mathematics, MDPI, vol. 9(18), pages 1-17, September.
    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.
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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. Shuyu Li & Yunsick Sung, 2023. "Transformer-Based Seq2Seq Model for Chord Progression Generation," Mathematics, MDPI, vol. 11(5), pages 1-14, February.

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