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Literature survey of multi-track music generation model based on generative confrontation network in intelligent composition

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Abstract

The production of traditional music is too complicated, consuming a lot of financial and human resources. Therefore, this paper aims to use artificial intelligence (AI) for songwriting and to explore the development and application of the Generative Adversarial Network (GAN) in smart music. An improved GAN-based Multi-Track Music (MTM)-GAN is established. The model is validated with the generation of 5 different music tracks for bass, drums, guitar, piano, and strings. The verification results are compared with the music generated by the existing Multi-Track Sequential GAN (MuseGAN) index evaluation method. The results show that many music clips generated by the MTM-GAN model are smooth and have a certain artistic aesthetic effect. Through the comparison of the two convergence curves of MuseGAN and MTM-GAN, when the penalty term is increased, the MTM-GAN of Consistency Term (CT) converges faster, and the training process is more stable. The numerical space of the parameter distribution obtained by the MTM-GAN-based music segment test is significantly smaller than that of MuseGAN. The probability of MTM-GAN overfitting is small. 62.8% of music listeners cannot distinguish the generated melody from the real melody. Therefore, the proposed model has the advantages of a more stable, more realistic, and faster fitting speed in music generation, indicating that the music generation method is effective.

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Acknowledgements

The authors acknowledge the help from the university colleagues.

Funding

This work was supported by the diversified reform of music and ear teaching in ordinary higher music majors (Grant no. 2017 Xiangcheng Institute Fa No.120.33).

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  1. College of Art, Hunan City University, Yiyang, 413000, China

    Weiming Liu

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  1. Weiming Liu
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Cite this article

Liu, W. Literature survey of multi-track music generation model based on generative confrontation network in intelligent composition. J Supercomput 79, 6560–6582 (2023). https://doi.org/10.1007/s11227-022-04914-5

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