# High-Resolution Sustain Pedal Depth Estimation from Piano Audio Across Room Acoustics

arXiv: https://arxiv.org/abs/2507.04230 · HTML: https://mlsanity.com/paper/2507.04230/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 19 references that are in the map.
- First posted on arXiv 6 Jul 2025.

## Builds on (most cited first, 7 of 7)

- [Enabling Factorized Piano Music Modeling and Generation with the MAESTRO Dataset](https://mlsanity.com/paper/1810.12247/) (2018)
- [High-resolution Piano Transcription with Pedals by Regressing Onset and Offset Times](https://mlsanity.com/paper/2010.01815/) (2020)
- [Beat this! Accurate beat tracking without DBN postprocessing](https://mlsanity.com/paper/2407.21658/) (2024)
- [Scoring Time Intervals using Non-Hierarchical Transformer For Automatic Piano Transcription](https://mlsanity.com/paper/2404.09466/) (2024)
- [Modeling Beats and Downbeats with a Time-Frequency Transformer](https://mlsanity.com/paper/2205.14701/) (2022)
- [Beat Transformer: Demixed Beat and Downbeat Tracking with Dilated Self-Attention](https://mlsanity.com/paper/2209.07140/) (2022)
- [Transfer Learning for Piano Sustain-Pedal Detection](https://mlsanity.com/paper/2103.13219/) (2021)

## Abstract (arXiv)

Piano sustain pedal detection has previously been approached as a binary on/off classification task, limiting its application in real-world piano performance scenarios where pedal depth significantly influences musical expression. This paper presents a novel approach for high-resolution estimation that predicts continuous pedal depth values. We introduce a Transformer-based architecture that not only matches state-of-the-art performance on the traditional binary classification task but also achieves high accuracy in continuous pedal depth estimation. Furthermore, by estimating continuous values, our model provides musically meaningful predictions for sustain pedal usage, whereas baseline models struggle to capture such nuanced expressions with their binary detection approach. Additionally, this paper investigates the influence of room acoustics on sustain pedal estimation using a synthetic dataset that includes varied acoustic conditions. We train our model with different combinations of room settings and test it in an unseen new environment using a "leave-one-out" approach. Our findings show that the two baseline models and ours are not robust to unseen room conditions. Statistical analysis further confirms that reverberation influences model predictions and introduces an overestimation bias.