MADB: A Large-Scale Music Aesthetics Dataset with Professional and Multi-Dimensional Annotations

research work·dataset paper·active

Research work examining evaluation benchmark, music/audio understanding.

Recorded facts

Official site	https://arxiv.org/abs/2607.06929 ↗
Geography	Global
arxiv	2607.06929
venue	arXiv preprint
authors	Sirui Zhang; Tianle Wang; Xinyi Tong; Peiyang Yu; Jishang Chen; Liangke Zhao; Haoxin Zhang; Duo Xu; Xin Jin; Feng Yu; Songchun Zhu
methods	benchmark evaluation
code urls	https://github.com/knownree/madb
demo urls	unknown
exact title	MADB: A Large-Scale Music Aesthetics Dataset with Professional and Multi-Dimensional Annotations
project urls	unknown
original title	unknown
citation counts	unknown
research topics	evaluation benchmark; music/audio understanding
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	2026-07-08
abstract level neutral summary	The work studies evaluation benchmark, music/audio understanding; methods and evaluation details are in the official abstract.
datasets benchmarks models tools used	unknown
correction withdrawal retraction status	No withdrawal marker observed in captured arXiv metadata.