MARBLE: Music Audio Representation Benchmark for Universal Evaluation

research work·benchmark study·active

Research work examining evaluation benchmark, music retrieval.

Recorded facts

Official site	https://arxiv.org/abs/2306.10548 ↗
Geography	Global
arxiv	2306.10548
venue	camera-ready version for NeurIPS 2023
authors	Ruibin Yuan; Yinghao Ma; Yizhi Li; Ge Zhang; Xingran Chen; Hanzhi Yin; Le Zhuo; Yiqi Liu; Jiawen Huang; Zeyue Tian; Binyue Deng; Ningzhi Wang; Chenghua Lin; Emmanouil Benetos; Anton Ragni; Norbert Gyenge; Roger Dannenberg; Wenhu Chen; Gus Xia; Wei Xue; Si Liu; Shi Wang; Ruibo Liu; Yike Guo; Jie Fu
methods	benchmark evaluation; language modeling; retrieval
code urls	unknown
demo urls	unknown
exact title	MARBLE: Music Audio Representation Benchmark for Universal Evaluation
project urls	https://marble-bm.shef.ac.uk
original title	unknown
citation counts	unknown
research topics	evaluation benchmark; music retrieval; music/audio representation learning; music/audio understanding
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	2023-06-18
abstract level neutral summary	The work studies evaluation benchmark, music retrieval; 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.