Towards Assessing Data Replication in Music Generation with Music Similarity Metrics on Raw Audio

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 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 of the training set. We evaluate the ability of five selected metrics to identify replication, by conducting a controlled replication experiment in different music genres based on 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 developers and users concerning data replication, highlighting the importance of ethical, social, legal and economic consequences of generative AI in the music domain. Code and examples are available for reproducibility purposes. .

LIAO Weihsiang;  SERRA Xavier;  MITSUFUJI Yuki;  GOMEZ Emilia; 

BATLLE Roser; 

2025-03-27

International Society for Music Information Retrieval

JRC138868

978-1-7327299-4-0 (online),