Exploiting the Two-Dimensional Nature of Agnostic Music Notation for Neural Optical Music Recognition
Department of Software and Computing Systems, University of Alicante, Ctra. San Vicente del Raspeig s/n, 03690 Alicante, Spain
*
Author to whom correspondence should be addressed.
†
The authors contributed equally to this work.
Academic Editor: Junhong Park
Appl. Sci. 2021, 11(8), 3621; https://doi.org/10.3390/app11083621
Received: 22 February 2021
/
Revised: 31 March 2021
/
Accepted: 15 April 2021
/
Published: 17 April 2021
(This article belongs to the Special Issue Advances in Music Reading Systems)
State-of-the-art Optical Music Recognition (OMR) techniques follow an end-to-end or holistic approach, i.e., a sole stage for completely processing a single-staff section image and for retrieving the symbols that appear therein. Such recognition systems are characterized by not requiring an exact alignment between each staff and their corresponding labels, hence facilitating the creation and retrieval of labeled corpora. Most commonly, these approaches consider an agnostic music representation, which characterizes music symbols by their shape and height (vertical position in the staff). However, this double nature is ignored since, in the learning process, these two features are treated as a single symbol. This work aims to exploit this trademark that differentiates music notation from other similar domains, such as text, by introducing a novel end-to-end approach to solve the OMR task at a staff-line level. We consider two Convolutional Recurrent Neural Network (CRNN) schemes trained to simultaneously extract the shape and height information and to propose different policies for eventually merging them at the actual neural level. The results obtained for two corpora of monophonic early music manuscripts prove that our proposal significantly decreases the recognition error in figures ranging between 14.4% and 25.6% in the best-case scenarios when compared to the baseline considered.
View Full-Text
Keywords:
optical music recognition; deep learning; connectionist temporal classification; agnostic music notation; sequence labeling
▼
Show Figures
Figure 1
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
MDPI and ACS Style
Alfaro-Contreras, M.; Valero-Mas, J.J. Exploiting the Two-Dimensional Nature of Agnostic Music Notation for Neural Optical Music Recognition. Appl. Sci. 2021, 11, 3621. https://doi.org/10.3390/app11083621
AMA Style
Alfaro-Contreras M, Valero-Mas JJ. Exploiting the Two-Dimensional Nature of Agnostic Music Notation for Neural Optical Music Recognition. Applied Sciences. 2021; 11(8):3621. https://doi.org/10.3390/app11083621
Chicago/Turabian StyleAlfaro-Contreras, María, and Jose J. Valero-Mas. 2021. "Exploiting the Two-Dimensional Nature of Agnostic Music Notation for Neural Optical Music Recognition" Applied Sciences 11, no. 8: 3621. https://doi.org/10.3390/app11083621
Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.
