PIANO MUSIC TRANSCRIPTION MODELING NOTE TEMPORAL EVOLUTION

Automatic music transcription (AMT) is the process of converting an acoustic musical signal into a symbolic musical representation such as a MIDI piano roll, which contains the pitches, the onsets and offsets of the notes and, possibly, their dynamic and source (i.e., instrument). Existing algorithms for AMT commonly iden-tify pitches and their saliences in each frame and then form notes in a post-processing stage, which applies a combination of threshold-ing, pruning and smoothing operations. Very few existing methods consider the note temporal evolution over multiple frames during the pitch identification stage. In this work we propose a note-based spectrogram factorization method that uses the entire temporal evo-lution of piano notes as a template dictionary. The method uses an artificial neural network to detect note onsets from the audio spec-tral flux. Next, it estimates the notes present in each audio segment between two successive onsets with a greedy search algorithm. Fi-nally, the spectrogram of each segment is factorized using a discrete combination of note templates comprised of full note spectrograms of individual piano notes sampled at different dynamic levels. We also propose a new psychoacoustically informed measure for spec-trogram similarity. Index Terms — Automatic music transcription, multi-pitch esti-mation, spectrogram factorization, onset detectio