发表机构
Universitat Pompeu Fabra; Music Technology Group（庞培法布拉大学; 音乐技术组）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对现有生成式音乐系统评估方法的局限，提出以音乐家为中心的MusGU+框架，评估10款系统并开发交互式筛选工具，助力音乐家理性选用生成式音乐AI。
AI 中文摘要
生成式音乐系统日益被宣传为能让音乐创作大众化的工具，但其对音乐家的实际适用性却未得到充分探索。现有研究包括以开放性为核心的评估框架，如MusGO（Music-Generative Open AI），以及对音乐家使用生成式系统体验的定性研究。然而，这些方法无法支持系统性比较，也无法为创意用途早期发现相关模型。受此局限驱动，我们提出MusGU+——一个以音乐家为中心的评估框架，围绕三个维度构建：适应性、可用性与可控性。这些维度共同衡量模型是否可在个人数据上进行可行的训练或微调、是否能集成到实际音乐工作流中、是否能以音乐层面有意义的方式被控制。我们评估了10个代表性生成式音乐系统，并开发了一个交互式发现工具，支持音乐家依据上述标准探索和筛选模型。尽管MusGO对推动负责任的研究实践仍有价值，但MusGU+能帮助音乐家理性选择并实际采用生成式系统。
英文摘要
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.
CommentsAccepted at AIMC 2026
