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Automatic Melody Composition Using Enhanced GAN

Author

Listed:
  • Shuyu Li

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

  • Sejun Jang

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

  • Yunsick Sung

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

Abstract

In traditional music composition, the composer has a special knowledge of music and combines emotion and creative experience to create music. As computer technology has evolved, various music-related technologies have been developed. To create new music, a considerable amount of time is required. Therefore, a system is required that can automatically compose music from input music. This study proposes a novel melody composition method that enhanced the original generative adversarial network (GAN) model based on individual bars. Two discriminators were used to form the enhanced GAN model: one was a long short-term memory (LSTM) model that was used to ensure correlation between the bars, and the other was a convolutional neural network (CNN) model that was used to ensure rationality of the bar structure. Experiments were conducted using bar encoding and the enhanced GAN model to compose a new melody and evaluate the quality of the composition melody. In the evaluation method, the TFIDF algorithm was also used to calculate the structural differences between four types of musical instrument digital interface (MIDI) file (i.e., randomly composed melody, melody composed by the original GAN, melody composed by the proposed method, and the real melody). Using the TFIDF algorithm, the structures of the melody composed were compared by the proposed method with the real melody and the structure of the traditional melody was compared with the structure of the real melody. The experimental results showed that the melody composed by the proposed method had more similarity with real melody structure with a difference of only 8% than that of the traditional melody structure.

Suggested Citation

  • Shuyu Li & Sejun Jang & Yunsick Sung, 2019. "Automatic Melody Composition Using Enhanced GAN," Mathematics, MDPI, vol. 7(10), pages 1-13, September.
  • Handle: RePEc:gam:jmathe:v:7:y:2019:i:10:p:883-:d:269803
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    Citations

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    Cited by:

    1. Shuyu Li & Yunsick Sung, 2021. "INCO-GAN: Variable-Length Music Generation Method Based on Inception Model-Based Conditional GAN," Mathematics, MDPI, vol. 9(4), pages 1-16, February.
    2. 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.
    3. Arulsamy, Karen & Delaney, Liam, 2022. "The impact of automatic enrolment on the mental health gap in pension participation: Evidence from the UK," Journal of Health Economics, Elsevier, vol. 86(C).
    4. Sejun Jang & Shuyu Li & Yunsick Sung, 2020. "FastText-Based Local Feature Visualization Algorithm for Merged Image-Based Malware Classification Framework for Cyber Security and Cyber Defense," Mathematics, MDPI, vol. 8(3), pages 1-13, March.
    5. Hyewon Yoon & Shuyu Li & Yunsick Sung, 2021. "Style Transformation Method of Stage Background Images by Emotion Words of Lyrics," Mathematics, MDPI, vol. 9(15), pages 1-20, August.
    6. Wenkai Huang & Feng Zhan, 2023. "A Novel Probabilistic Diffusion Model Based on the Weak Selection Mimicry Theory for the Generation of Hypnotic Songs," Mathematics, MDPI, vol. 11(15), pages 1-26, July.
    7. 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.
    8. 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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