%0 Conference Proceedings
%T Optical Music Recognition by Recurrent Neural Networks
%A Arnau Baro
%A Pau Riba
%A Jorge Calvo-Zaragoza
%A Alicia Fornes
%B 14th IAPR International Workshop on Graphics Recognition
%D 2017
%F Arnau Baro2017
%O DAG; 600.097; 601.302; 600.121
%O exported from refbase (http://refbase.cvc.uab.es/show.php?record=3056), last updated on Fri, 21 Jan 2022 10:58:46 +0100
%X 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
%K Optical Music Recognition
%K Recurrent Neural Network
%K Long Short-Term Memory
%U http://refbase.cvc.uab.es/files/BRC2017.pdf
%U http://dx.doi.org/10.1109/ICDAR.2017.260
%P 25-26