mlsanityData to 7 Oct 2026

MusGO: A Community-Driven Framework For Assessing Openness in Music-Generative AI

arXiv 2507.03599 ↗First posted 4 Jul 2025cs.SD cs.AI cs.CYcited by 7

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Abstract (arXiv)

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.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 34 of its 56 references that are in the map. First posted on arXiv 4 Jul 2025.

Builds on

34 of 56 references.

References · 34

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22 more
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Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 7.

7 papers.

  1. 2026HumMusQA: A Human-written Music Understanding QA Benchmark Datasetcited by 1
  2. 2026MusGU+: Toward a Musician-Centered Evaluation Framework and Discovery Tool for Generative Music AIcited by 1
  3. 2026ConceptCaps: a Distilled Concept Dataset for Interpretability in Music Modelscited by 0
  4. 2026Training-Efficient Text-to-Music Generation with State-Space Modelingcited by 0
  5. 2026How Far Can Chord-Symbol Time-Series Adaptation Carry Genre Identity? Capabilities and Boundaries in Multi-Genre Chord-Symbol Modelingcited by 0
  6. 2026On the Human and Computer Alignment of Attribute-Based Music Matchescited by 0
  7. 2026SCAPES: Semantically Conditioned Autoregressive Prior for Environmental Soundscited by 0