Toward Part-Aware Choral Transcription with singing voice assignment

Hanyu Meng; Zhanhong He; Zixun Guo; Yaolong Ju

Home/Archive/arXiv

arXiv · 2610.09861

Toward Part-Aware Choral Transcription with singing voice assignment

Hanyu Meng·Zhanhong He·Zixun Guo·Yaolong Ju

View original source ↗Read source document ↗Export metadata ↓

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/.

Explore related subjects

eess.AS cs.SD

Keep this discovery

Explore connections, maps & timelines

BibTeX RIS

Hanyu Meng, Zhanhong He, Zixun Guo, Yaolong Ju. 2026-10-07. Toward Part-Aware Choral Transcription with singing voice assignment. https://arxiv.org/abs/2610.09861

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

Sound Field Interpolation Using Physics-Informed Extreme Learning Machine with Pre-Training

Numerous machine learning-based sound field interpolation methods have been proposed. In particular, physics-informed neural networks (PINNs) can accurately interpolate sound fields from a small number of microphones. However, their high computational cost and long training time pose practical challenges for applications requiring real-time processing or online learning. To address this, we propose a hybrid framework that combines PINN-based pre-training with a physics-informed extreme learning machine (PIELM) tailored for acoustic fields. By replacing iterative PINN fine-tuning for each target sound field with closed-form output-layer adaptation using hidden-layer weights pre-trained by PINN, the proposed method efficiently interpolates unknown sound fields from limited observations. Simulation results under simplified one-dimensional free-field conditions demonstrate that, given a pre-trained model, the proposed method achieves interpolation accuracy comparable to that of PINN-based fine-tuning while reducing the adaptation time by more than three orders of magnitude.

eess.AS↗

arXiv2026

CTC-Seeded Token Edit Refinement for Non-Autoregressive Speech Recognition

Non-autoregressive automatic speech recognition (ASR) enables parallel decoding, but many refinement-based methods begin from random, fully masked, or fixed-length token sequences, requiring multiple iterations to reconstruct the complete transcript. We instead formulate ASR decoding as a variable-length edit refinement of a greedy connectionist temporal classification (CTC) hypothesis. An acoustic-conditioned Edit Flow decoder operates directly on the collapsed CTC hypothesis, predicting insertion, deletion, and substitution operations in parallel. The Edit Flow decoder is jointly trained with a CTC model using a continuous-time discrete diffusion loss. During inference, we find that just two edit steps yield substantial Word Error Rate (WER) reductions, and classifier-free guidance (CFG) further enhances recognition quality by focusing the model on audio features. We also constrain edit proposals using CTC confidence to improve accuracy. Finally, ablation studies validate our design choices, while decoder pretraining and pretrained encoder integration yield significant additional performance gains.

eess.AS↗

arXiv2026

NormToken: Speaker- and Duration-Normalized Semantic Speech Tokens via Iterative S2U-T2U Refinement

Semantic speech tokens should preserve linguistic content while suppressing utterance-specific acoustic and duration variation. However, existing speech-to-unit (S2U) tokenizers often retain speaker-related acoustic characteristics and duration information. To address this issue, we propose NormToken, an iterative semantic token purification framework that alternates S2U and text-to-unit (T2U) training. In each iteration, the T2U model produces text-derived tokens to supervise a newly initialized S2U tokenizer. The resulting S2U tokens are then used as targets for the next T2U iteration. This cycle drives the two models toward a shared, text-predictable token space. Experiments on Mandarin and English demonstrate improved S2U--T2U agreement and parallel-utterance token consistency. De-tokenizers trained on the initial and refined tokens further show that refined tokens maintain comparable WER and CER while improving speaker similarity in both voice cloning and text-to-speech synthesis. In voice cloning, refined tokens produce speaking rates closer to the acoustic reference, suggesting reduced dependence on source duration information. Audio samples are available at https://hanlin1004.github.io/normtoken_demopage.

eess.AS↗