# Revisiting Input Time-frequency Representations in Multi-pitch Estimation for Vocal Ensembles

arXiv: https://arxiv.org/abs/2610.03656 · HTML: https://mlsanity.com/paper/2610.03656/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 30 references that are in the map.
- First posted on arXiv 2 Oct 2026.

## Builds on (most cited first, 14 of 14)

- [A ConvNet for the 2020s](https://mlsanity.com/paper/2201.03545/) (2022)
- [Conformer: Convolution-augmented Transformer for Speech Recognition](https://mlsanity.com/paper/2005.08100/) (2020)
- [An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling](https://mlsanity.com/paper/1803.01271/) (2018)
- [ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders](https://mlsanity.com/paper/2301.00808/) (2023)
- [CREPE: A Convolutional Representation for Pitch Estimation](https://mlsanity.com/paper/1802.06182/) (2018)
- [Onsets and Frames: Dual-Objective Piano Transcription](https://mlsanity.com/paper/1710.11153/) (2017)
- [InceptionNeXt: When Inception Meets ConvNeXt](https://mlsanity.com/paper/2303.16900/) (2023)
- [RMVPE: A Robust Model for Vocal Pitch Estimation in Polyphonic Music](https://mlsanity.com/paper/2306.15412/) (2023)
- [A Lightweight Instrument-Agnostic Model for Polyphonic Note Transcription and Multipitch Estimation](https://mlsanity.com/paper/2203.09893/) (2022)
- [Simultaneous Separation and Transcription of Mixtures with Multiple Polyphonic and Percussive Instruments](https://mlsanity.com/paper/1910.12621/) (2019)
- [Deep Learning Based Source Separation Applied To Choir Ensembles](https://mlsanity.com/paper/2008.07645/) (2020)
- [Multiple F0 Estimation in Vocal Ensembles using Convolutional Neural Networks](https://mlsanity.com/paper/2009.04172/) (2020)
- [JaCappella Corpus: A Japanese a Cappella Vocal Ensemble Corpus](https://mlsanity.com/paper/2211.16028/) (2022)
- [ChoralSynth: Synthetic Dataset of Choral Singing](https://mlsanity.com/paper/2311.08350/) (2023)

## Abstract (arXiv)

Multi-pitch estimation in vocal ensembles is challenging because singers occupy overlapping pitch ranges and often sing at closely spaced fundamental frequencies, causing their harmonics to overlap in time-frequency representations. Existing models commonly use harmonic constant-Q transform (HCQT)-based representations to provide frequency-adaptive resolution, at the cost of expensive feature extraction when training mixtures are generated on the fly. We revisit this design and compare HCQT with a linear short-time Fourier transform (STFT), whose frequency bins are directly provided as model inputs. Despite its fixed frequency resolution and the absence of a pitch-aligned input grid, the linear STFT outperforms HCQT while substantially reducing feature-extraction cost. Further analysis shows that a longer analysis window or broader spectral coverage provides no additional improvement, while restricting the input to the predicted pitch range reduces the advantage of the linear STFT. These results suggest that finer frequency resolution does not necessarily improve vocal-ensemble MPE, and that shorter analysis windows can be more effective for time-varying vocal pitches.