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

research work·provenance detection paper·active

Research work examining evaluation benchmark, music/audio representation learning.

Recorded facts

Official site	https://arxiv.org/abs/2606.08663 ↗
Geography	Global
arxiv	2606.08663
venue	Accepted to ICML 2026 ML4Audio workshop
authors	Joonyong Park; Jungwoo Kim; Junyoung Koh; Yuki Saito
methods	benchmark evaluation
code urls	https://github.com/MAAP-LAB/CoMoE
demo urls	unknown
exact title	Probing Token Spaces under Generator Shift in AI-Generated Music Detection
project urls	unknown
original title	unknown
citation counts	unknown
research topics	evaluation benchmark; music/audio representation learning; synthetic-audio detection
peer review status	venue or acceptance claim recorded in arXiv comment
disclosed conflicts	unknown
stated contribution	Presents or evaluates the system, method, benchmark, or analysis identified in the paper title.
us market scope basis	Included as a materially relevant public research artifact in the US-facing AI-music ecosystem; direct affiliation varies.
funding acknowledgements	unknown
affiliations at publication	unknown
publication or preprint date	2026-06-07
abstract level neutral summary	The work studies evaluation benchmark, music/audio representation learning; methods and evaluation details are in the official abstract.
datasets benchmarks models tools used	MERT; Suno; Udio
correction withdrawal retraction status	No withdrawal marker observed in captured arXiv metadata.