  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.
