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End-to-End Neural Optical Music Recognition of Monophonic Scores

by 1,2,* and 3,4
Schulich School of Music, McGill University, Montreal, QC H3A 1E3, Canada
PRHLT Research Center, Universitat Politècnica de València, 46022 Valencia, Spain
Instituto Superior de Enseñanzas Artísticas, 03690 Alicante, Spain
Departamento de Lenguajes y Sistemas Informáticos, Universidad de Alicante, 03690 Alicante, Spain
Author to whom correspondence should be addressed.
Appl. Sci. 2018, 8(4), 606;
Received: 28 February 2018 / Revised: 22 March 2018 / Accepted: 8 April 2018 / Published: 11 April 2018
(This article belongs to the Special Issue Digital Audio and Image Processing with Focus on Music Research)
Optical Music Recognition is a field of research that investigates how to computationally decode music notation from images. Despite the efforts made so far, there are hardly any complete solutions to the problem. In this work, we study the use of neural networks that work in an end-to-end manner. This is achieved by using a neural model that combines the capabilities of convolutional neural networks, which work on the input image, and recurrent neural networks, which deal with the sequential nature of the problem. Thanks to the use of the the so-called Connectionist Temporal Classification loss function, these models can be directly trained from input images accompanied by their corresponding transcripts into music symbol sequences. We also present the Printed Music Scores dataset, containing more than 80,000 monodic single-staff real scores in common western notation, that is used to train and evaluate the neural approach. In our experiments, it is demonstrated that this formulation can be carried out successfully. Additionally, we study several considerations about the codification of the output musical sequences, the convergence and scalability of the neural models, as well as the ability of this approach to locate symbols in the input score. View Full-Text
Keywords: Optical Music Recognition; end-to-end recognition; Deep Learning; music score images Optical Music Recognition; end-to-end recognition; Deep Learning; music score images
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MDPI and ACS Style

Calvo-Zaragoza, J.; Rizo, D. End-to-End Neural Optical Music Recognition of Monophonic Scores. Appl. Sci. 2018, 8, 606.

AMA Style

Calvo-Zaragoza J, Rizo D. End-to-End Neural Optical Music Recognition of Monophonic Scores. Applied Sciences. 2018; 8(4):606.

Chicago/Turabian Style

Calvo-Zaragoza, Jorge, and David Rizo. 2018. "End-to-End Neural Optical Music Recognition of Monophonic Scores" Applied Sciences 8, no. 4: 606.

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