Music ControlNet: Multiple Time-varying Controls for Music Generation

research work·research paper·active

Research work examining evaluation benchmark, music generation.

Recorded facts

Official site	https://arxiv.org/abs/2311.07069 ↗
Geography	Global
arxiv	2311.07069
venue	11 pages, 4 figure, 5 tables, Submitted to IEEE/ACM Transactions on Audio, Speech, and Language Processing (TASLP)
authors	Shih-Lun Wu; Chris Donahue; Shinji Watanabe; Nicholas J. Bryan
methods	benchmark evaluation; diffusion modeling
code urls	unknown
demo urls	https://MusicControlNet.github.io/web/
exact title	Music ControlNet: Multiple Time-varying Controls for Music Generation
project urls	https://MusicControlNet.github.io/web/
original title	unknown
citation counts	unknown
research topics	evaluation benchmark; 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	Carnegie Mellon University; Adobe Research
publication or preprint date	2023-11-13
abstract level neutral summary	The work studies evaluation benchmark, music generation; methods and evaluation details are in the official abstract.
datasets benchmarks models tools used	MusicGen
correction withdrawal retraction status	No withdrawal marker observed in captured arXiv metadata.