\section{Conclusion}
\label{sec:conclusion}

We introduced TS-RaMIA, a practical auditing framework enabling artists and rights holders to test for unauthorized use of their works in training generative music models.
By exploiting structural tokens (bar lines, positions, tempo) through sample-level analysis, rigorous debiasing, and tail-of-top-$k$ aggregation, TS-RaMIA achieves AUC 0.826 and TPR@1\%FPR 14.6\% on a 67M-parameter REMI Transformer, substantially outperforming debiased baselines (AUC 0.563).
Cross-representation validation on NotaGen (ABC notation) demonstrates method transferability.
We release the complete protocol to support transparent copyright auditing and encourage future work on defenses, larger-scale validation, and approximations for generation-only APIs.


\section{Acknowledgements}
This work was supported by the Jiangsu Science and Technology Programme (Major Special Programme, Grant No. BG2024027), the Suzhou Science and Technology Development Planning Programme (Gusu Innovation and Entrepreneurship Leading Talents Program, Grant No. ZXL2022472), and the XJTLU Research Development Fund (Grant No. RDF-22-02-046).
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