MAP-Music2Vec: A Simple and Effective Baseline for Self-Supervised Music Audio Representation Learning

research work·research paper·active

Research work examining music/audio representation learning.

Recorded facts

Official site	https://arxiv.org/abs/2212.02508 ↗
Geography	Global
arxiv	2212.02508
venue	arXiv preprint
authors	Yizhi Li; Ruibin Yuan; Ge Zhang; Yinghao Ma; Chenghua Lin; Xingran Chen; Anton Ragni; Hanzhi Yin; Zhijie Hu; Haoyu He; Emmanouil Benetos; Norbert Gyenge; Ruibo Liu; Jie Fu
methods	self-supervised learning
code urls	https://huggingface.co/m-a-p/music2vec-v1
demo urls	https://huggingface.co/m-a-p/music2vec-v1
exact title	MAP-Music2Vec: A Simple and Effective Baseline for Self-Supervised Music Audio Representation Learning
project urls	unknown
original title	unknown
citation counts	unknown
research topics	music/audio representation learning
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	2022-12-05
abstract level neutral summary	The work studies music/audio representation learning; 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.