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

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

URL: http://arxiv.org/abs/2608.30940v1
Date: Mon, 31 Aug 2026 15:12:51 GMT
Title: MusGU+: Toward a Musician-Centered Evaluation Framework and Discovery Tool for Generative Music AI
Abstract summary: Generative music systems are increasingly presented as tools that democratize music creation, yet their practical suitability for musicians remains underexplored.<n>We introduce MusGU+, a musician-centered evaluation framework organized around three dimensions: Adaptability, Usability, and Controllability.<n>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.
Score: 9.16460001215725
License: http://creativecommons.org/licenses/by/4.0/
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.

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