HAIM: Human-AI Music Datasets for AI Music Production Tracking Benchmark

research work·benchmark study·active

Research work examining evaluation benchmark, synthetic-audio detection.

Recorded facts

Official site	https://arxiv.org/abs/2606.01686 ↗
Geography	Global
arxiv	2606.01686
venue	arXiv preprint
authors	Seonghyeon Go; Yumin Kim
methods	benchmark evaluation
code urls	unknown
demo urls	unknown
exact title	HAIM: Human-AI Music Datasets for AI Music Production Tracking Benchmark
project urls	unknown
original title	unknown
citation counts	unknown
research topics	evaluation benchmark; synthetic-audio detection
peer review status	not established from abstract metadata
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	2026-06-01
abstract level neutral summary	The work studies evaluation benchmark, synthetic-audio detection; methods and evaluation details are in the official abstract.
datasets benchmarks models tools used	Suno; Udio
correction withdrawal retraction status	No withdrawal marker observed in captured arXiv metadata.