MERT: Acoustic Music Understanding Model with Large-Scale Self-supervised Training

research work·research paper·active

Research work examining music/audio understanding.

Recorded facts

Official site	https://arxiv.org/abs/2306.00107 ↗
Geography	Global
arxiv	2306.00107
venue	accepted by ICLR 2024
authors	Yizhi Li; Ruibin Yuan; Ge Zhang; Yinghao Ma; Xingran Chen; Hanzhi Yin; Chenghao Xiao; Chenghua Lin; Anton Ragni; Emmanouil Benetos; Norbert Gyenge; Roger Dannenberg; Ruibo Liu; Wenhu Chen; Gus Xia; Yemin Shi; Wenhao Huang; Zili Wang; Yike Guo; Jie Fu
methods	language modeling; self-supervised learning
code urls	unknown
demo urls	unknown
exact title	MERT: Acoustic Music Understanding Model with Large-Scale Self-supervised Training
project urls	unknown
original title	unknown
citation counts	unknown
research topics	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	2023-05-31
abstract level neutral summary	The work studies music/audio understanding; methods and evaluation details are in the official abstract.
datasets benchmarks models tools used	MERT
correction withdrawal retraction status	No withdrawal marker observed in captured arXiv metadata.

introduces	MERT v1 95M 2023-05-31 — nowSource 1 ↗ VERIFIED high confidence
introduces	MERT v1 330M 2023-05-31 — nowSource 1 ↗ VERIFIED high confidence