Museformer: Transformer with Fine- and Coarse-Grained Attention for Music Generation

research work·research paper·active

Research work examining music generation, symbolic music.

Recorded facts

Official site	https://arxiv.org/abs/2210.10349 ↗
Geography	Global
arxiv	2210.10349
venue	Accepted by the Thirty-sixth Conference on Neural Information Processing Systems (NeurIPS 2022)
authors	Botao Yu; Peiling Lu; Rui Wang; Wei Hu; Xu Tan; Wei Ye; Shikun Zhang; Tao Qin; Tie-Yan Liu
methods	transformer architecture
code urls	unknown
demo urls	unknown
exact title	Museformer: Transformer with Fine- and Coarse-Grained Attention for Music Generation
project urls	unknown
original title	unknown
citation counts	unknown
research topics	music generation; symbolic music
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	2022-10-19
abstract level neutral summary	The work studies music generation, symbolic music; 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.