Harmonic-Aware Frequency and Time Attention for Automatic Piano Transcription

Abstract

Automatic music transcription (AMT) is to transcribe music audio into note symbol representations. Concurrent notes overlapping in the frequency and time domains still hinder the performance of polyphonic piano transcription in current studies. In this work, we develop an attention-based method for piano transcription, where we propose a harmonic-aware attention to capture the musical frequency structure, and a local time attention to model temporal dependencies. The harmonic-aware frequency attention not only emphasizes the relationship between the obvious harmonics, but also extracts the correlation in the residual non-harmonic component. The time attention mechanism is improved using the learnable attention range masks to model frame-wise short-term dependencies on different subtasks. Experiments on the MAESTRO dataset demonstrate that the proposed system achieves state-of-the-art transcription performance on both frame-wise and note-wise F1 metrics. Considering the influence of the piano pedals' dynamic behavior on note duration, a note duration modification method is also proposed. With a more accurate annotation of the offset on MAESTRO, the transcription performance is further improved.