About • How To Use • Citations • Acknowledgments • License
This GitHub repository contains the implementation of the Sheet Music Transfomrmer (SMT), novel model for Optical Music Recognition (OMR) beyond monophonic level transcription. Unlike traditional approaches that primarily leverage monophonic transcription techniques for complex score layouts, the SMT model overcomes these limitations by offering a robust image-to-sequence solution for transcribing polyphonic musical scores directly from images.
Usage instructions coming soon
@misc{riosvila2024SMT,
title = {Sheet Music Transformer: End-To-End Optical Music Recognition Beyond Monophonic Transcription},
author = {Antonio Ríos-Vila and Jorge Calvo-Zaragoza and Thierry Paquet},
year = 2024,
eprint = {2402.07596},
archiveprefix = {arXiv},
primaryclass = {cs.CV}
}This work is part of the I+D+i PID2020-118447RA-I00 (MultiScore) project, funded by MCIN/AEI/10.13039/501100011033. Computational resources were provided by the Valencian Government and FEDER funding through IDIFEDER/2020/003.
This work is under a MIT license.