1. Towards Assessing Data Replication in Music Generation with Music Similarity Metrics on Raw Audio
- Author
-
Batlle-Roca, Roser, Liao, Wei-Hisang, Serra, Xavier, Mitsufuji, Yuki, and Gómez, Emilia
- Subjects
Computer Science - Sound ,Computer Science - Artificial Intelligence ,Computer Science - Multimedia ,Electrical Engineering and Systems Science - Audio and Speech Processing - Abstract
Recent advancements in music generation are raising multiple concerns about the implications of AI in creative music processes, current business models and impacts related to intellectual property management. A relevant discussion and related technical challenge is the potential replication and plagiarism of the training set in AI-generated music, which could lead to misuse of data and intellectual property rights violations. To tackle this issue, we present the Music Replication Assessment (MiRA) tool: a model-independent open evaluation method based on diverse audio music similarity metrics to assess data replication. We evaluate the ability of five metrics to identify exact replication by conducting a controlled replication experiment in different music genres using synthetic samples. Our results show that the proposed methodology can estimate exact data replication with a proportion higher than 10%. By introducing the MiRA tool, we intend to encourage the open evaluation of music-generative models by researchers, developers, and users concerning data replication, highlighting the importance of the ethical, social, legal, and economic consequences. Code and examples are available for reproducibility purposes., Comment: Accepted at ISMIR 2024
- Published
- 2024