Towards Assessing Data Replication in Music Generation with Music Similarity Metrics on Raw Audio
<|reference_start|>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 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.<|reference_end|>
@article{batlle-roca2024towards,
    title={Towards Assessing Data Replication in Music Generation with Music
  Similarity Metrics on Raw Audio},
    author={Roser Batlle-Roca, Wei-Hisang Liao, Xavier Serra, Yuki Mitsufuji and
  Emilia G'omez},
    journal={arXiv preprint arXiv:2407.14364},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.14364},
    primaryClass={cs.SD cs.AI cs.MM eess.AS}
}