# High-resolution Piano Transcription with Pedals by Regressing Onset and Offset Times

arXiv: https://arxiv.org/abs/2010.01815 · HTML: https://mlsanity.com/paper/2010.01815/

## Key facts

- Cited by 61 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Oct 2020.

## Built on by (most cited first, 30 of 61)

- [MusicBERT: Symbolic Music Understanding with Large-Scale Pre-Training](https://mlsanity.com/paper/2106.05630/) (2021)
- [MT3: Multi-Task Multitrack Music Transcription](https://mlsanity.com/paper/2111.03017/) (2021)
- [Sequence-to-Sequence Piano Transcription with Transformers](https://mlsanity.com/paper/2107.09142/) (2021)
- [Unaligned Supervision For Automatic Music Transcription in The Wild](https://mlsanity.com/paper/2204.13668/) (2022)
- [The Piano Inpainting Application](https://mlsanity.com/paper/2107.05944/) (2021)
- [Automatic Piano Transcription with Hierarchical Frequency-Time Transformer](https://mlsanity.com/paper/2307.04305/) (2023)
- [A Data-Driven Analysis of Robust Automatic Piano Transcription](https://mlsanity.com/paper/2402.01424/) (2024)
- [High Resolution Guitar Transcription via Domain Adaptation](https://mlsanity.com/paper/2402.15258/) (2024)
- [Scoring Time Intervals using Non-Hierarchical Transformer For Automatic Piano Transcription](https://mlsanity.com/paper/2404.09466/) (2024)
- [DiffRoll: Diffusion-based Generative Music Transcription with Unsupervised Pretraining Capability](https://mlsanity.com/paper/2210.05148/) (2022)
- [BERT-like Pre-training for Symbolic Piano Music Classification Tasks](https://mlsanity.com/paper/2107.05223/) (2021)
- [CLaMP 2: Multimodal Music Information Retrieval Across 101 Languages Using Large Language Models](https://mlsanity.com/paper/2410.13267/) (2024)
- [The Chamber Ensemble Generator: Limitless High-Quality MIR Data via Generative Modeling](https://mlsanity.com/paper/2209.14458/) (2022)
- [Pop2Piano : Pop Audio-based Piano Cover Generation](https://mlsanity.com/paper/2211.00895/) (2022)
- [F"urElise: Capturing and Physically Synthesizing Hand Motions of Piano Performance](https://mlsanity.com/paper/2410.05791/) (2024)
- [Aria-MIDI: A Dataset of Piano MIDI Files for Symbolic Music Modeling](https://mlsanity.com/paper/2504.15071/) (2025)
- [Scaling Self-Supervised Representation Learning for Symbolic Piano Performance](https://mlsanity.com/paper/2506.23869/) (2025)
- [Reconstructing Human Expressiveness in Piano Performances with a Transformer Network](https://mlsanity.com/paper/2306.06040/) (2023)
- [Jointist: Joint Learning for Multi-instrument Transcription and Its Applications](https://mlsanity.com/paper/2206.10805/) (2022)
- [Music Foundation Model as Generic Booster for Music Downstream Tasks](https://mlsanity.com/paper/2411.01135/) (2024)
- [Unified Cross-modal Translation of Score Images, Symbolic Music, and Performance Audio](https://mlsanity.com/paper/2505.12863/) (2025)
- [GOAT: A Large Dataset of Paired Guitar Audio Recordings and Tablatures](https://mlsanity.com/paper/2509.22655/) (2025)
- [FretNet: Continuous-Valued Pitch Contour Streaming for Polyphonic Guitar Tablature Transcription](https://mlsanity.com/paper/2212.03023/) (2022)
- [A Phoneme-Informed Neural Network Model for Note-Level Singing Transcription](https://mlsanity.com/paper/2304.05917/) (2023)
- [N-Gram Unsupervised Compoundation and Feature Injection for Better Symbolic Music Understanding](https://mlsanity.com/paper/2312.08931/) (2023)
- [PianoMotion10M: Dataset and Benchmark for Hand Motion Generation in Piano Performance](https://mlsanity.com/paper/2406.09326/) (2024)
- [PianoVAM: A Multimodal Piano Performance Dataset](https://mlsanity.com/paper/2509.08800/) (2025)
- [Playing Technique Detection by Fusing Note Onset Information in Guzheng Performance](https://mlsanity.com/paper/2209.08774/) (2022)
- [Towards Efficient and Real-Time Piano Transcription Using Neural Autoregressive Models](https://mlsanity.com/paper/2404.06818/) (2024)
- [Jointist: Simultaneous Improvement of Multi-instrument Transcription and Music Source Separation via Joint Training](https://mlsanity.com/paper/2302.00286/) (2023)

## Abstract (arXiv)

Automatic music transcription (AMT) is the task of transcribing audio recordings into symbolic representations. Recently, neural network-based methods have been applied to AMT, and have achieved state-of-the-art results. However, many previous systems only detect the onset and offset of notes frame-wise, so the transcription resolution is limited to the frame hop size. There is a lack of research on using different strategies to encode onset and offset targets for training. In addition, previous AMT systems are sensitive to the misaligned onset and offset labels of audio recordings. Furthermore, there are limited researches on sustain pedal transcription on large-scale datasets. In this article, we propose a high-resolution AMT system trained by regressing precise onset and offset times of piano notes. At inference, we propose an algorithm to analytically calculate the precise onset and offset times of piano notes and pedal events. We show that our AMT system is robust to the misaligned onset and offset labels compared to previous systems. Our proposed system achieves an onset F1 of 96.72% on the MAESTRO dataset, outperforming previous onsets and frames system of 94.80%. Our system achieves a pedal onset F1 score of 91.86%, which is the first benchmark result on the MAESTRO dataset. We have released the source code and checkpoints of our work at https://github.com/bytedance/pianotranscription.