A Survey of AI Music Generation Tools and Models

作者：YueYue Zhu, Jared Baca, Banafsheh Rekabdar, R. Rawassizadeh · 发表于：arXiv.org · 年份：2023 · DOI：10.48550/arxiv.2308.12982 · 被引用次数：27 · 研究领域：Computer Science、Engineering

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.