arXiv:2605.29202v1 Announce Type: new Abstract: Recent advances in text-to-music generation enable high-fidelity synthesis of structured musical audio, raising growing concerns about data provenance, consent, and training transparency. These models are typically trained on large-scale corpora with little disclosure, leaving no practical mechanism to verify whether a particular audio sample was included in training. In this paper, we investigate black-box membership inference for generative music models, aiming to determine whether a candidate music sample was used during training, given only q
The proliferation of generative AI models across various modalities, including music, necessitates immediate attention to data provenance and ethical use, prompting research into auditing mechanisms.
Understanding the training data of generative music models is crucial for intellectual property rights, creator consent, and preventing the illicit exploitation of existing works, impacting the entire creative industry.
The ability to audit training data for generative music could establish new standards for transparency and accountability in large language model (LLM) and generative AI development, shifting power dynamics towards original content creators.
- · Original music artists and copyright holders
- · Auditing and AI ethics firms
- · Regulatory bodies
- · AI developers prioritizing ethical data practices
- · Developers using undisclosed or unconsented training data
- · Generative AI models with opaque data practices
- · Platforms hosting infringing AI-generated content
Black-box membership inference tools directly enable the identification of specific musical samples within a generative AI model's training dataset.
This capability could lead to legal challenges against AI models trained on copyrighted material without consent, forcing a re-evaluation of data acquisition strategies.
The establishment of transparent data provenance as a industry standard could lead to new business models for licensed training data, fundamentally restructuring the supply chain for generative AI content.
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