Make-An-Audio: Text-To-Audio Generation with Prompt-Enhanced Diffusion Models

research work·research paper·active

Research work examining audio generation, evaluation benchmark.

Recorded facts

Official site	https://arxiv.org/abs/2301.12661 ↗
Geography	Global
arxiv	2301.12661
venue	Audio samples are available at https://Text-to-Audio.github.io
authors	Rongjie Huang; Jiawei Huang; Dongchao Yang; Yi Ren; Luping Liu; Mingze Li; Zhenhui Ye; Jinglin Liu; Xiang Yin; Zhou Zhao
methods	benchmark evaluation; contrastive learning; diffusion modeling; self-supervised learning
code urls	unknown
demo urls	https://Text-to-Audio.github.io
exact title	Make-An-Audio: Text-To-Audio Generation with Prompt-Enhanced Diffusion Models
project urls	https://Text-to-Audio.github.io
original title	unknown
citation counts	unknown
research topics	audio generation; evaluation benchmark; music/audio representation learning; text-to-audio 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	2023-01-30
abstract level neutral summary	The work studies audio generation, evaluation benchmark; methods and evaluation details are in the official abstract.
datasets benchmarks models tools used	CLAP
correction withdrawal retraction status	No withdrawal marker observed in captured arXiv metadata.