High-resolution Piano Transcription with Pedals by Regressing Onset and Offset Times
This work addresses the need for high-resolution and robust piano transcription, including pedal events, which is incremental but offers specific improvements for music processing applications.
The paper tackles the problem of low-resolution automatic music transcription (AMT) for piano by proposing a system that regresses precise onset and offset times, achieving an onset F1 of 96.72% on the MAESTRO dataset, outperforming previous methods, and providing the first benchmark for pedal transcription with a score of 91.86%.
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/piano_transcription.