Título:
MusGU+ : Toward a musician-centered evaluation framework and discovery tool for generative music AI.
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Autor:
Ibáñez-Martínez, Laura
Batlle-Roca, Roser
Serra, Xavier
Rocamora, Martín
Batlle-Roca, Roser
Serra, Xavier
Rocamora, Martín
Tutor:
Tipo de documento:
Ponencia
Editor:
Palabras clave:
Artificial Intelligence
Künstliche Intelligenz
Music
Musik
Creativity
Kreativität
AIMC2026
Künstliche Intelligenz
Music
Musik
Creativity
Kreativität
AIMC2026
Descriptores:
Año de publicación:
2026
Contenido:
Resumen:
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.
Descripción:
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Editorial:
AIMC
EN:
7th Conference on AI Music Creativity (AIMC 2026), Berlin, Germany, 16-18 sep 2026, pp. 1-10, DOI: 10.5281/zenodo.22281933.
Financiadores:
Este trabajo ha contado con el apoyo de «IA y Música : Cátedra en Inteligencia Artificial y Música» (TSI-100929-2023-1), financiado por la Secretaría de Estado de Digitalización e Inteligencia Artificial, los fondos Next Generation EU de la Unión Europea y BMAT Music Innovators, así como de IMPA : Multimodal AI for Audio Processing (PID2023-152250OB-I00), financiado por el Ministerio de Ciencia, Innovación y Universidades del Gobierno de España y la Agencia Estatal de Investigación (AEI), y cofinanciado por la Unión Europea.
Citación:
Ibáñez-Martínez, L., Batlle-Roca, R., Serra, X. y otros. MusGU+ : Toward a musician-centered evaluation framework and discovery tool for generative music AI [en línea]. EN: 7th Conference on AI Music Creativity (AIMC 2026), Berlin, Germany, 16-18 sep 2026, pp 1-10. DOI: 10.5281/zenodo.22281933.
Citación:
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Departamento académico:
Procesamiento de Señales
Grupo de investigación:
Procesamiento de Audio
Licencia:
Attribution-NonCommercial-NoDerivatives 4.0 International
Colecciones:
| Ficheros | Descripción | Tamaño | Formato | ||
|---|---|---|---|---|---|
| IBSR26.pdf | Versión definitiva | 329.92 KB | Adobe PDF |
Licencia Creative Commons:
Excepto donde se indique lo contrario, la licencia de este ítem se describe como Attribution-NonCommercial-NoDerivatives 4.0 International