Exploring Transformer's potential on automatic piano transcription
Abstract
Most recent research about automatic music transcription (AMT) uses convolutional neural networks and recurrent neural networks to model the mapping from music signals to symbolic notation. Based on a high-resolution piano transcription system, we explore the possibility of incorporating another powerful sequence transformation tool -- the Transformer -- to deal with the AMT problem. We argue that the properties of the Transformer make it more suitable for certain AMT subtasks. We confirm the Transformer's superiority on the velocity detection task by experiments on the MAESTRO dataset and a cross-dataset evaluation on the MAPS dataset. We observe a performance improvement on both frame-level and note-level metrics after introducing the Transformer network.
Cite
@article{arxiv.2204.03898,
title = {Exploring Transformer's potential on automatic piano transcription},
author = {Longshen Ou and Ziyi Guo and Emmanouil Benetos and Jiqing Han and Ye Wang},
journal= {arXiv preprint arXiv:2204.03898},
year = {2022}
}
Comments
Accepted by ICASSP 2022