Onsets and Frames: Dual-Objective Piano Transcription
Abstract
We advance the state of the art in polyphonic piano music transcription by using a deep convolutional and recurrent neural network which is trained to jointly predict onsets and frames. Our model predicts pitch onset events and then uses those predictions to condition framewise pitch predictions. During inference, we restrict the predictions from the framewise detector by not allowing a new note to start unless the onset detector also agrees that an onset for that pitch is present in the frame. We focus on improving onsets and offsets together instead of either in isolation as we believe this correlates better with human musical perception. Our approach results in over a 100% relative improvement in note F1 score (with offsets) on the MAPS dataset. Furthermore, we extend the model to predict relative velocities of normalized audio which results in more natural-sounding transcriptions.
Cite
@article{arxiv.1710.11153,
title = {Onsets and Frames: Dual-Objective Piano Transcription},
author = {Curtis Hawthorne and Erich Elsen and Jialin Song and Adam Roberts and Ian Simon and Colin Raffel and Jesse Engel and Sageev Oore and Douglas Eck},
journal= {arXiv preprint arXiv:1710.11153},
year = {2018}
}
Comments
Examples available at https://goo.gl/magenta/onsets-frames-examples