Toward Universal Text-to-Music Retrieval

research work·research paper·active

Research work examining evaluation benchmark, music retrieval.

Recorded facts

Official site	https://arxiv.org/abs/2211.14558 ↗
Geography	Global
arxiv	2211.14558
venue	arXiv preprint
authors	SeungHeon Doh; Minz Won; Keunwoo Choi; Juhan Nam
methods	benchmark evaluation; retrieval
code urls	unknown
demo urls	unknown
exact title	Toward Universal Text-to-Music Retrieval
project urls	unknown
original title	unknown
citation counts	unknown
research topics	evaluation benchmark; music retrieval; music/audio representation learning; text-to-music generation
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	2022-11-26
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.