@InProceedings{ArnauBaro2017,
author="Arnau Baro
and Pau Riba
and Jorge Calvo-Zaragoza
and Alicia Fornes",
title="Optical Music Recognition by Recurrent Neural Networks",
booktitle="14th IAPR International Workshop on Graphics Recognition",
year="2017",
pages="25--26",
optkeywords="Optical Music Recognition",
optkeywords="Recurrent Neural Network",
optkeywords="Long Short-Term Memory",
abstract="Optical Music Recognition is the task of transcribing a music score into a machine readable format. Many music scores are written in a single staff, and therefore, they could be treated as a sequence. Therefore, this work explores the use of Long Short-Term Memory (LSTM) Recurrent Neural Networks for reading the music score sequentially, where the LSTM helps in keeping the context. For training, we have used a synthetic dataset of more than 40000 images, labeled at primitive level",
optnote="DAG; 600.097; 601.302; 600.121",
optnote="exported from refbase (http://refbase.cvc.uab.es/show.php?record=3056), last updated on Fri, 21 Jan 2022 10:58:46 +0100",
doi="10.1109/ICDAR.2017.260",
file=":http://refbase.cvc.uab.es/files/BRC2017.pdf:PDF"
}