Transition-Aware: A More Robust Approach for Piano Transcription
Piano transcription is a classic problem in music information retrieval. More and more transcription methods based on deep learning have been proposed in recent years. In 2019, Google Brain
published a larger piano transcription dataset, MAESTRO. On this
dataset, Onsets and Frames transcription approach proposed by
Hawthorne achieved a stunning onset F1 score of 94.73%. Unlike
the annotation method of Onsets and Frames, Transition-aware
model presented in this paper annotates the attack process of piano
signals called atack transition in multiple frames, instead of only
marking the onset frame. In this way, the piano signals around
onset time are taken into account, enabling the detection of piano onset more stable and robust. Transition-aware achieves a
higher transcription F1 score than Onsets and Frames on MAESTRO dataset and MAPS dataset, reducing many extra note detection errors. This indicates that Transition-aware approach has
better generalization ability on different datasets.