MusGU+: Toward a Musician-Centered Evaluation Framework and Discovery Tool for Generative Music AI
2026-08-31 • Sound
SoundArtificial IntelligenceComputers and Society
AI summaryⓘ
The authors point out that while many generative music tools claim to make music making easier for everyone, it's unclear how well these tools actually work for musicians in real life. To help with this, they created MusGU+, a way to rate music generation systems based on how easy they are to adapt, use, and control in real music projects. They tested 10 music systems and made a tool that helps musicians find models that fit their needs. Their work aims to help musicians choose the right generative tools instead of just focusing on responsible AI research.
generative music systemsmusic creationMusGU+MusGOadaptabilityusabilitycontrollabilitymodel evaluationmusic workflowsinteractive discovery tool
Authors
Laura Ibáñez-Martínez, Roser Batlle-Roca, Xavier Serra, Martín Rocamora
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.