mlsanityData to 14 Oct 2026

A Survey of AI Music Generation Tools and Models

arXiv 2308.12982 ↗First posted 24 Aug 2023cs.SD cs.AI cs.HCcited by 9

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Abstract (arXiv)

In this work, we provide a comprehensive survey of AI music generation tools, including both research projects and commercialized applications. To conduct our analysis, we classified music generation approaches into three categories: parameter-based, text-based, and visual-based classes. Our survey highlights the diverse possibilities and functional features of these tools, which cater to a wide range of users, from regular listeners to professional musicians. We observed that each tool has its own set of advantages and limitations. As a result, we have compiled a comprehensive list of these factors that should be considered during the tool selection process. Moreover, our survey offers critical insights into the underlying mechanisms and challenges of AI music generation.

Key facts. Cited by 9 papers in the map (data through 14 Oct 2026). Builds on 42 of its 101 references that are in the map. First posted on arXiv 24 Aug 2023.

Builds on

42 of 101 references.

References · 42

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30 more
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 3; 2025: 5; 2026: 1.

9 papers.