MusGU+: Toward a Musician-Centered Evaluation Framework and Discovery Tool for Generative Music AI

2026-08-31

AI Summary

为解决生成音乐AI对音乐家的实际适用性问题，提出MusGU+框架，从适应性、可用性和可控性三方面评估并帮助音乐家选择合适的模型。

Abstract

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.

Problem

• Generative Music Systems
• Musician-Centered Evaluation
• Adoption of AI in Music

Innovation

• Adaptability
• Usability
• Controllability
• Generative Music AI
• Musician-Centered Evaluation