A Survey of AI Music Generation Tools and Models

• URL: http://arxiv.org/abs/2308.12982v1
• Date: Thu, 24 Aug 2023 00:49:08 GMT
• Title: A Survey of AI Music Generation Tools and Models
• Authors: Yueyue Zhu, Jared Baca, Banafsheh Rekabdar, Reza Rawassizadeh
• Abstract summary: 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. Our survey offers critical insights into the underlying mechanisms and challenges of AI music generation.
• Score: 0.9421843976231371
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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