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Automatic music transcription (AMT) is the task of transcribing audiorecordings into symbolic representations. Recently, neural network-basedmethods have been applied to AMT, and have achieved state-of-the-art results.However, many previous systems only detect the onset and offset of notesframe-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 andoffset targets for training. In addition, previous AMT systems are sensitive tothe misaligned onset and offset labels of audio recordings. Furthermore, thereare limited researches on sustain pedal transcription on large-scale datasets.In this article, we propose a high-resolution AMT system trained by regressingprecise onset and offset times of piano notes. At inference, we propose analgorithm to analytically calculate the precise onset and offset times of pianonotes and pedal events. We show that our AMT system is robust to the misalignedonset and offset labels compared to previous systems. Our proposed systemachieves an onset F1 of 96.72% on the MAESTRO dataset, outperforming previousonsets and frames system of 94.80%. Our system achieves a pedal onset F1 scoreof 91.86\%, which is the first benchmark result on the MAESTRO dataset. We havereleased the source code and checkpoints of our work athttps://github.com/bytedance/piano_transcription.

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