Next Article in Journal
Synthesis of Zeolite A from Metakaolin and Its Application in the Adsorption of Cationic Dyes
Next Article in Special Issue
Deep Neural Networks for Document Processing of Music Score Images
Previous Article in Journal
Study of the Electromagnetic Properties of Nano (MxZn1−x)Fe2O4 (M=Cu, Ni) as a Function of the Sintering Temperature
Previous Article in Special Issue
A Novel Tempogram Generating Algorithm Based on Matching Pursuit
Article Menu
Issue 4 (April) cover image

Export Article

Open AccessArticle
Appl. Sci. 2018, 8(4), 606; https://doi.org/10.3390/app8040606

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

1
Schulich School of Music, McGill University, Montreal, QC H3A 1E3, Canada
2
PRHLT Research Center, Universitat Politècnica de València, 46022 Valencia, Spain
3
Instituto Superior de Enseñanzas Artísticas, 03690 Alicante, Spain
4
Departamento de Lenguajes y Sistemas Informáticos, Universidad de Alicante, 03690 Alicante, Spain
*
Author to whom correspondence should be addressed.
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)
Full-Text   |   PDF [3681 KB, uploaded 3 May 2018]   |  

Abstract

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
Figures

Graphical abstract

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

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

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Appl. Sci. EISSN 2076-3417 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top