| title | Toward Part-Aware Choral Transcription with singing voice assignment |
|---|---|
| authors | Hanyu Meng, Zhanhong He, Zixun Guo, Yaolong Ju |
| date | 2026-10-07 |
| venue | arXiv (preprint, not peer reviewed) |
| id | arXiv:2610.09861 |
| affiliation | UNSW |
| upvotes | 11 (Hugging Face Daily Papers, 2026-11-01) |
| links | abs pdf hf code |
| abstract | Single-instrument automatic music transcription (AMT) has advanced substantially, yet choral applications require soprano, alto, tenor, and bass (SATB) to be transcribed as separate parts. Recent note-level choral AMT instead produces a single merged note track, limiting rehearsal, education, and score reconstruction. To address this limitation, we introduce Part-aware Choral Transcription (PawCT), to our knowledge the first end-to-end neural framework that identifies active SATB parts from choral audio and transcribes each into a separate note-level track. PawCT combines part-specific onset, offset, and frame prediction with part-presence estimation, union-level supervision, and structured training targets using a range prior (RP) based on SATB pitch ranges and its ordered-continuity (OC) extension, which adds within-part melodic continuity and cross-part pitch ordering. On YouChorale, PawCT-RP-OC achieves a macro part-aware note F1 of 0.225 at a 50-ms onset tolerance, outperforming an adapted choral baseline (0.165) by 36.4% relative and a two-stage post-hoc assignment pipeline (0.175). Its part-agnostic variant, PagCT, achieves a 50-ms onset F1 of 0.382, compared with 0.237 for the previous state-of-the-art choral AMT model. Cross-dataset evaluations on CSD and Cantoria further assess performance under dataset shift. These results demonstrate the benefit of jointly modeling note transcription and vocal-part assignment. Code and demos are available at https://hanyu-meng.github.io/Paw_Choral_AMT_Demo/. |
cite
@misc{meng2026toward,
title={Toward Part-Aware Choral Transcription with singing voice assignment},
author={Hanyu Meng and Zhanhong He and Zixun Guo and Yaolong Ju},
year={2026},
eprint={2610.09861},
archivePrefix={arXiv},
url={https://arxiv.org/abs/2610.09861}
}