Music ControlNet: Multiple Time-varying Controls for Music Generation
research work·research paper·active
Research work examining evaluation benchmark, music generation.
Verified 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. |
Current
| affiliated with | Adobe 2023-11-13 — now |
|---|
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