

\begin{abstract}
Artists and rights holders face growing concerns about unauthorized use of their copyrighted works in training generative models.
We introduce \textbf{TS-RaMIA}, a practical auditing framework enabling creators to test whether their symbolic music has been used without authorization.
Unlike existing likelihood-based approaches that are confounded by piece length and density, TS-RaMIA exploits \emph{structural tokens}—bar lines, positions, and tempo markers encoding musical phrasing—through sample-level analysis and rigorous debiasing.
Our method combines (i) length matching and conditional calibration to remove spurious confounders, (ii) tail-of-top-$k$ aggregation on structural tokens to amplify sparse memorization signals, and (iii) a lightweight meta-attacker fusing statistical cues via composer-stratified cross-validation.
Evaluated on a 67M-parameter REMI Transformer trained on MAESTRO pieces, TS-RaMIA achieves AUC 0.826 and TPR@1\%FPR 14.6\%, while a debiased baseline drops to AUC 0.563.
Cross-representation validation on NotaGen (ABC notation) yields comparable performance (AUC 0.73, TPR@1\%FPR 8.9\%), demonstrating transferability.
We release our code at https://github.com/kaslim/TS-RaMIA.
\end{abstract}

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% - Reframed from copyright/creator perspective
% - Method, mechanisms, numbers included
% - NotaGen integrated (not separated)
% - ~150 words
