A Lightweight Instrument-Agnostic Model for Polyphonic Note Transcription and Multipitch Estimation
research work·research paper·active
Research work examining evaluation benchmark, music transcription.
Verified facts
| Official site | https://arxiv.org/abs/2203.09893 ↗ |
|---|---|
| Geography | Global |
| arxiv | 2203.09893 |
| venue | arXiv preprint |
| authors | Rachel M. Bittner; Juan José Bosch; David Rubinstein; Gabriel Meseguer-Brocal; Sebastian Ewert |
| methods | benchmark evaluation |
| code urls | unknown |
| demo urls | unknown |
| exact title | A Lightweight Instrument-Agnostic Model for Polyphonic Note Transcription and Multipitch Estimation |
| project urls | unknown |
| original title | unknown |
| citation counts | unknown |
| research topics | evaluation benchmark; music transcription |
| 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 | Spotify |
| publication or preprint date | 2022-03-18 |
| abstract level neutral summary | The work studies evaluation benchmark, music transcription; 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. |
Current
| affiliated with | Spotify 2022-03-18 — now |
|---|
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