Moûsai: Text-to-Music Generation with Long-Context Latent Diffusion

research work·research paper·active

Research work examining music generation, text-to-music generation.

Recorded facts

Official site	https://arxiv.org/abs/2301.11757 ↗
Geography	Global
arxiv	2301.11757
venue	arXiv preprint
authors	Flavio Schneider; Ojasv Kamal; Zhijing Jin; Bernhard Schölkopf
methods	diffusion modeling
code urls	https://github.com/archinetai/audio-diffusion-pytorch
demo urls	https://bit.ly/audio-diffusion; https://github.com/archinetai/audio-diffusion-pytorch
exact title	Moûsai: Text-to-Music Generation with Long-Context Latent Diffusion
project urls	http://bit.ly/44ozWDH; https://bit.ly/audio-diffusion
original title	unknown
citation counts	unknown
research topics	music generation; 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	2023-01-27
abstract level neutral summary	The work studies music generation, text-to-music generation; 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.