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Joint Estimation of Note Values and Voices for Audio-to-Score Piano Transcription

This paper describes an essential improvement of a stateof-the-art automatic piano transcription (APT) system that can transcribe a human-readable symbolic musical score from a piano recording. Whereas estimation of the pitches and onset times of musical notes has been improved drastically thanks to the recent advances of deep learning, estimation of note values and voice labels, which is a crucial component of the APT system, still remains a challenging task. A previous study has revealed that (i) the pitches and onset times of notes are useful but the performed note durations are less informative for estimating the note values and that (ii) the note values and voices have mutual dependency. We thus propose a bidirectional long short-term memory network that jointly estimates note values and voice labels from note pitches and onset times estimated in advance. To improve the robustness against tempo errors, extra notes, and missing notes included in the input data, we investigate data augmentation. The experimental results show the efficacy of multi-task learning and data augmentation, and the proposed method achieved better accuracies than existing methods.