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A review of intelligent music generation systems

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Abstract

With the introduction of ChatGPT, the public’s perception of AI-generated content has begun to reshape. Artificial intelligence has significantly reduced the barrier to entry for non-professionals in creative endeavors, enhancing the efficiency of content creation. Recent advancements have seen significant improvements in the quality of symbolic music generation, which is enabled by the use of modern generative algorithms to extract patterns implicit in a piece of music based on rule constraints or a musical corpus. Nevertheless, existing literature reviews tend to present a conventional and conservative perspective on future development trajectories, with a notable absence of thorough benchmarking of generative models. This paper provides a survey and analysis of recent intelligent music generation techniques, outlining their respective characteristics and discussing existing methods for evaluation. Additionally, the paper compares the different characteristics of music generation techniques in the East and West as well as analysing the field’s development prospects.

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Acknowledgements

Figure 4-11 are cited from relevant paper on music generation, and we extend our sincere appreciation to the authors for their contribution.

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Authors and Affiliations

  1. College of Electric and Information Engineering, Tongji University, Cao’an Street, Shanghai, 201804, China

    Lei Wang, Ziyi Zhao, Hanwei Liu, Junwei Pang & Qidi Wu

  2. Department of Music Engineering, Shanghai Conservatory of Music, Fenyang street, Shanghai, 200031, China

    Yi Qin

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Contributions

Methodology: LW; Formal analysis and investigation: ZZ; Writing—original draft preparation: ZZ, HL; Data curation: JP; Writing—review and editing: YQ, SL, ZZ; Supervision: QW, LW; Funding acquisition: LW.

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Correspondence toZiyi Zhao.

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