Multitask Learning for Polyphonic Piano Transcription, a Case Study

Rainer Kelz, Sebastian Böck, Gerhard Widmer

Viewing polyphonic piano transcription as a multitask learning problem, where we need to simultaneously predict onsets, intermediate frames and offsets of notes, we investigate the performance impact of additional prediction targets, using a variety of suitable convolutional neural network architectures. We quantify performance differences of additional objectives on the large MAESTRO dataset.

Knowledge Graph

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