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.

翻译：本文对人工智能音乐生成工具进行了全面综述，涵盖研究项目与商业化应用。为开展分析，我们将音乐生成方法分为三类：基于参数、基于文本和基于视觉的方法。本综述突出了这些工具在功能特性上的多样性，它们能够满足从普通听众到专业音乐人的广泛用户群体需求。我们观察到每种工具都有其独特的优势与局限性，据此整理了一份在工具选择过程中应予以考量的综合因素清单。此外，本综述深入揭示了人工智能音乐生成的内在机制与挑战。