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arXiv · 1902.04390

Multitask Learning for Polyphonic Piano Transcription, a Case Study

Rainer Kelz·Sebastian Böck·Gerhard Widmer
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Abstract

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.

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Rainer Kelz, Sebastian Böck, Gerhard Widmer. 2019-02-12. Multitask Learning for Polyphonic Piano Transcription, a Case Study. https://arxiv.org/abs/1902.04390

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