为音乐人设计的AI音乐生成模型评估与发现工具
MusGU+: Toward a Musician-Centered Evaluation Framework and Discovery Tool for Generative Music AI
- 从可适配性、可用性和可控性三维度评估音乐生成模型
- 测试10个主流模型，支持按音乐创作需求筛选
- 适合音乐创作者快速找到适合自己工作流的AI工具
生成式音乐系统被宣传为民主化音乐创作的工具，但其对音乐人的实际适用性仍缺乏深入研究。现有工作包括以开放性为核心的评估框架（如MusGO），以及对音乐家使用体验的定性研究，但这些方法无法支持系统的比较或早期模型发现。为此，我们提出MusGU+，一个以音乐人为中心的评估框架，涵盖可适配性、可用性和可控性三个维度，分别衡量模型是否可基于个人数据训练/微调、是否能融入真实音乐工作流、是否具备有意义的音乐控制能力。我们评估了10个代表性生成音乐系统，并开发了一个交互式发现工具，帮助音乐人根据上述标准探索和筛选模型。尽管MusGO在推动负责任研究方面仍具价值，MusGU+更有利于音乐人做出知情选择并实现生成系统的实际应用。
原文摘要 · Abstract (English)
Generative music systems are increasingly presented as tools that democratize music creation, yet their practical suitability for musicians remains underexplored. Prior work includes openness-focused evaluation frameworks, such as MusGO (Music-Generative Open AI), as well as qualitative studies of musicians' experiences with generative systems. However, these approaches do not support systematic comparison or early-stage discovery of models for creative use. Motivated by such limitations, we introduce MusGU+, a musician-centered evaluation framework organized around three dimensions: Adaptability, Usability, and Controllability. Together, these capture whether a model can be feasibly trained or fine-tuned on personal data, integrated into real-world music workflows, and controlled in musically meaningful ways. We evaluate 10 representative generative music systems and present an interactive discovery tool that enables musicians to explore and filter models according to these criteria. While MusGO remains valuable for promoting responsible research practices, MusGU+ supports informed selection and practical adoption of generative systems by musicians.
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