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.

我们提供了一篇综合调查报告，介绍了包括研究项目和商业应用在内的人工智能音乐生成工具。通过对音乐生成方法进行分类，我们发现参数化、基于文本和基于视觉的三种方法。调查重点展示了这些工具的多样性和功能特点，适用于普通听众和专业音乐人。每个工具都有自己的优点和限制，因此我们编制了一份综合列表，用于在选择过程中考虑这些因素。此外，我们的调查揭示了人工智能音乐生成的底层机制和挑战。