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) ...
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 modelindependent 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.
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