Noise2Music: Text-conditioned Music Generation with Diffusion Models

research work·research paper·active

Research work examining music generation, music/audio representation learning.

Verified facts

Official site	https://arxiv.org/abs/2302.03917 ↗
Geography	Global
arxiv	2302.03917
venue	15 pages
authors	Qingqing Huang; Daniel S. Park; Tao Wang; Timo I. Denk; Andy Ly; Nanxin Chen; Zhengdong Zhang; Zhishuai Zhang; Jiahui Yu; Christian Frank; Jesse Engel; Quoc V. Le; William Chan; Zhifeng Chen; Wei Han
methods	diffusion modeling; language modeling
code urls	unknown
demo urls	https://google-research.github.io/noise2music
exact title	Noise2Music: Text-conditioned Music Generation with Diffusion Models
project urls	https://google-research.github.io/noise2music
original title	unknown
citation counts	unknown
research topics	music generation; 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	Google Research
publication or preprint date	2023-02-08
abstract level neutral summary	The work studies music generation, 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.