Multitask Learning for Polyphonic Piano Transcription, a Case Study
Sound
2019-02-13 v1 Audio and Speech Processing
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.
Keywords
Cite
@article{arxiv.1902.04390,
title = {Multitask Learning for Polyphonic Piano Transcription, a Case Study},
author = {Rainer Kelz and Sebastian Böck and Gerhard Widmer},
journal= {arXiv preprint arXiv:1902.04390},
year = {2019}
}