GETMusic: Generating Any Music Tracks with a Unified Representation and Diffusion Framework
research work·research paper·active
Research work examining music generation, music/audio representation learning.
Verified facts
| Official site | https://arxiv.org/abs/2305.10841 ↗ |
|---|---|
| Geography | Global |
| arxiv | 2305.10841 |
| venue | 13 pages, 4 figures |
| authors | Ang Lv; Xu Tan; Peiling Lu; Wei Ye; Shikun Zhang; Jiang Bian; Rui Yan |
| methods | diffusion modeling |
| code urls | unknown |
| demo urls | unknown |
| exact title | GETMusic: Generating Any Music Tracks with a Unified Representation and Diffusion Framework |
| project urls | unknown |
| original title | unknown |
| citation counts | unknown |
| research topics | music generation; music/audio representation learning; symbolic music |
| 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 | 2023-05-18 |
| 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. |
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