@Inbook{ArnauBaro2018,
author="Arnau Baro
and Pau Riba
and Jorge Calvo-Zaragoza
and Alicia Fornes",
editor="A. Fornes, B. Lamiroy",
chapter="Optical Music Recognition by Long Short-Term Memory Networks",
title="Graphics Recognition. Current Trends and Evolutions",
year="2018",
publisher="Springer",
volume="11009",
pages="81--95",
optkeywords="Optical Music Recognition",
optkeywords="Recurrent Neural Network",
optkeywords="Long ShortTerm Memory",
abstract="Optical Music Recognition refers to the task of transcribing the image of 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. The experimental results are promising, showing the benefits of our approach.",
optnote="DAG; 600.097; 601.302; 601.330; 600.121",
optnote="exported from refbase (http://refbase.cvc.uab.es/show.php?record=3227), last updated on Thu, 29 Oct 2020 13:39:11 +0100",
isbn="978-3-030-02283-9",
doi="10.1007/978-3-030-02284-6_7",
file=":http://refbase.cvc.uab.es/files/BRC2018.pdf:PDF"
}