MusicLDM: Enhancing Novelty in Text-to-Music Generation Using Beat-Synchronous Mixup Strategies

research work·research paper·active

Research work examining audio generation, music generation.

Verified facts

Official site	https://arxiv.org/abs/2308.01546 ↗
Geography	Global
arxiv	2308.01546
venue	16 pages, 3 figures, 2 tables, demo page: https://musicldm.github.io/
authors	Ke Chen; Yusong Wu; Haohe Liu; Marianna Nezhurina; Taylor Berg-Kirkpatrick; Shlomo Dubnov
methods	contrastive learning; diffusion modeling
code urls	unknown
demo urls	https://musicldm.github.io/
exact title	MusicLDM: Enhancing Novelty in Text-to-Music Generation Using Beat-Synchronous Mixup Strategies
project urls	https://musicldm.github.io/
original title	unknown
citation counts	unknown
research topics	audio generation; music generation; text-to-audio 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	2023-08-03
abstract level neutral summary	The work studies audio generation, music generation; methods and evaluation details are in the official abstract.
datasets benchmarks models tools used	AudioLDM; CLAP
correction withdrawal retraction status	No withdrawal marker observed in captured arXiv metadata.