MusGU+: Toward a Musician-Centered Evaluation Framework and Discovery Tool for Generative Music AI
cs.SD, cs.AI, cs.CY, eess.AS
Submitted: 2026-08-31
Updated: 2026-08-31
Comments: Accepted at AIMC 2026
Code: https://github.com/lauraibnz/MusGU-plus
Project page: https://lauraibnz.github.io/MusGU-plus
License: http://creativecommons.org/licenses/by/4.0/
The gist: Generative music systems are increasingly presented as tools that democratize music creation, yet their practical suitability for musicians remains underexplored.
Terminology
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.
Sources
- Jukebox: A Generative Model for Music
- DDSP: Differentiable Digital Signal Processing
- MusicLM: Generating Music From Text
- AudioGen: Textually Guided Audio Generation
- RAVE: A variational autoencoder for fast and high-quality neural audio synthesis
- Simple and Controllable Music Generation
- Workflow-Based Evaluation of Music Generation Systems
- JAM: A Tiny Flow-based Song Generator with Fine-grained Controllability and Aesthetic Alignment
- YuE: Scaling Open Foundation Models for Long-Form Music Generation
- Music and Artificial Intelligence: Artistic Trends
- Opening Musical Creativity? Embedded Ideologies in Generative-AI Music Systems
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