  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.
