Probing Token Spaces under Generator Shift in AI-Generated Music Detection

arXiv CS Tuesday 09 June 2026, 04:00 UTC By Joonyong Park, Jungwoo Kim, Junyoung Koh, Yuki Saito 1 min read

Key Points

arXiv:2606.08663v1 Announce Type: new Abstract: AI-generated music detectors can appear robust on standard benchmark splits, yet their deployments require transfer to generator sources absent during training. We study this problem with source-restricted evaluation on \textsc{MoM-open}, an open reconstruction of MoM-CLAM that replaces the non-redistributable real corpus with FMA and MTG-Jamendo while preserving the fake-generator protocol. To isolate the role of representation, we introduce \textsc{CoMoE}, a compact fixed classifier for comparing heterogeneous audio token spaces while keeping the downstream architecture and training recipe unchanged. Experiments show that standard and real-source-restricted splits are nearly saturated, whereas fake-source restriction exposes large differences between token spaces: X-Codec tokens are strongest when training on Udio alone, while MERT-derived tokens are stronger when training on Suno-v3.5 alone. These results suggest that codec-style discrete token spaces should be treated as a primary experimental axis under generator shift in AI-generated music detection. Our code and data are available at https://github.com/MAAP-LAB/CoMoE.

AI-Generated Music Detection (ORG) FMA (ORG) MTG-Jamendo (ORG) Udio (ORG) MERT (ORG) Suno (PERSON)

Originally published by arXiv CS Read original →

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Probing Token Spaces under Generator Shift in AI-Generated Music Detection

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