MusGO: a community-driven framework for assessing openness in music-generative AI

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  • dc.contributor.author Batlle-Roca, Roser
  • dc.contributor.author Ibáñez-Martínez, Laura
  • dc.contributor.author Serra, Xavier
  • dc.contributor.author Gómez, Emilia
  • dc.contributor.author Rocamora, Martín
  • dc.date.accessioned 2025-09-05T06:26:56Z
  • dc.date.available 2025-09-05T06:26:56Z
  • dc.date.issued 2025
  • dc.description Comunicació presentada al 26th International Society for Music Information Retrieval Conference (ISMIR 2025), celebrada a Daejeon (Korea) del 21 al 25 de setembre del 2025
  • dc.description.abstract Since 2023, generative AI has rapidly advanced in the music domain. Despite significant technological advancements, music-generative models raise critical ethical challenges, including a lack of transparency and accountability, along with risks such as the replication of artists' works, which highlights the importance of fostering openness. With upcoming regulations such as the EU AI Act encouraging open models, many generative models are being released labelled as 'open'. However, the definition of an open model remains widely debated. In this article, we adapt a recently proposed evidence-based framework for assessing openness in LLMs to the music domain. Using feedback from a survey of 110 participants from the Music Information Retrieval (MIR) community, we refine the framework into MusGO (Music-Generative Open AI), which comprises 13 openness categories: 8 essential and 5 desirable. We evaluate 16 state-of-the-art generative models and provide an openness leaderboard that is fully open to public scrutiny and community contributions. Through this work, we aim to clarify the concept of openness in music-generative AI and promote its transparent and responsible development.
  • dc.description.sponsorship This work has been supported by IA y Música: Cátedra en Inteligencia Artificial y Música (TSI-100929-2023-1), funded by the Secretaría de Estado de Digitalización e Inteligencia Artificial and the European Union-Next Generation EU, and IMPA: Multimodal AI for Audio Processing (PID2023-152250OB-I00), funded by the Ministry of Science, Innovation and Universities of the Spanish Government, the Agencia Estatal de Investigación (AEI) and cofinanced by the European Union. We thank our colleagues at the Music Technology Group at Universitat Pompeu Fabra for their thoughtful insights, constructive discussions and active engagement throughout the development of this work
  • dc.format.mimetype application/pdf
  • dc.identifier.citation Batlle-Roca R, Ibáñez-Martínez L, Serra X, Gómez E, Rocamora M. MusGO: a community-driven framework for assessing openness in music-generative AI. Paper presented at: 26th International Society for Music Information Retrieval Conference (ISMIR 2025); 2025 Sep 21-25; Daejeon, Korea. 10p. DOI: 10.48550/arXiv.2507.03599
  • dc.identifier.uri http://hdl.handle.net/10230/71122
  • dc.language.iso eng
  • dc.publisher International Society for Music Information Retrieval (ISMIR)
  • dc.relation.projectID info:eu-repo/grantAgreement/ES/3PE/PID2023-152250OB-I00
  • dc.rights Licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0). Attribution: R. Batlle-Roca, L. Ibáñez-Martínez, X. Serra, E. Gómez, and M. Rocamora, “MusGO: A Community-Driven Framework for Assessing Openness in MusicGenerative AI”, in Proc. of the 26th Int. Society for Music Information Retrieval Conf., Daejeon, South Korea, 2025.
  • dc.rights.accessRights info:eu-repo/semantics/openAccess
  • dc.rights.uri http://creativecommons.org/licenses/by/4.0/
  • dc.subject.keyword MusGO
  • dc.subject.keyword Music-generative AI
  • dc.subject.keyword Openness
  • dc.title MusGO: a community-driven framework for assessing openness in music-generative AI
  • dc.type info:eu-repo/semantics/conferenceObject
  • dc.type.version info:eu-repo/semantics/publishedVersion