Advances in Music Reading Systems

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Acoustics and Vibrations".

Deadline for manuscript submissions: closed (10 December 2021) | Viewed by 7553
Related Workshop: 3rd International Workshop on Reading Music Systems.

Special Issue Editors

Software and Computing Systems, University of Alicante, 03690 Alicante, Spain
Interests: machine learning; computer vision; document image analysis; optical music recognition; music information retrieval
Institute of Information Systems Engineering, TU Wien, 1040 Vienna, Austria
Interests: music; machine learning; deep learning; optical music recognition; computer vision; music information retrieval

Special Issue Information

Dear colleagues,

Developing computational systems for reading music scores represents an attractive endeavor, which provides an efficient way of opening up the vast amount of existing written music for musicians and musicological research. In addition, the involved tasks represent exciting opportunities for research in the fields of computer science, machine learning, and computer vision.

While it might be true that the task of reading music shares similarities with others (for instance, the optical processing of text documents), music notation has enough nuances that call for specific solutions, which are not easily generalized from other domains. This Special Issue seeks advances for all kinds of computational systems that read music or deal with documents depicting music. These include, but are not limited to:

  • Optical music recognition
  • Information retrieval from music scores
  • Sheet music search
  • Score following
  • Image processing on music scores
  • Multi-modal systems involving sheet music
  • Writer/Hand identification
  • Novel input-methods to produce or edit written music
  • Applications related to sheet music and reading systems
  • Evaluation methods and protocols
  • Datasets and systems for creating large datasets
  • Application of existing technologies in new use-cases

Prof. Dr. Jorge Calvo-Zaragoza
Dr. Alexander Pacha
Guest Editors

Manuscript Submission Information

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Reading Music Systems
  • Music Score Images
  • Optical Music Recognition
  • Music Information Retrieval

Published Papers (3 papers)

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Research

23 pages, 3880 KiB  
Article
Applying Automatic Translation for Optical Music Recognition’s Encoding Step
by Antonio Ríos-Vila, Miquel Esplà-Gomis, David Rizo, Pedro J. Ponce de León and José M. Iñesta
Appl. Sci. 2021, 11(9), 3890; https://0-doi-org.brum.beds.ac.uk/10.3390/app11093890 - 25 Apr 2021
Cited by 6 | Viewed by 2000
Abstract
Optical music recognition is a research field whose efforts have been mainly focused, due to the difficulties involved in its processes, on document and image recognition. However, there is a final step after the recognition phase that has not been properly addressed or [...] Read more.
Optical music recognition is a research field whose efforts have been mainly focused, due to the difficulties involved in its processes, on document and image recognition. However, there is a final step after the recognition phase that has not been properly addressed or discussed, and which is relevant to obtaining a standard digital score from the recognition process: the step of encoding data into a standard file format. In this paper, we address this task by proposing and evaluating the feasibility of using machine translation techniques, using statistical approaches and neural systems, to automatically convert the results of graphical encoding recognition into a standard semantic format, which can be exported as a digital score. We also discuss the implications, challenges and details to be taken into account when applying machine translation techniques to music languages, which are very different from natural human languages. This needs to be addressed prior to performing experiments and has not been reported in previous works. We also describe and detail experimental results, and conclude that applying machine translation techniques is a suitable solution for this task, as they have proven to obtain robust results. Full article
(This article belongs to the Special Issue Advances in Music Reading Systems)
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16 pages, 3340 KiB  
Article
Exploiting the Two-Dimensional Nature of Agnostic Music Notation for Neural Optical Music Recognition
by María Alfaro-Contreras and Jose J. Valero-Mas
Appl. Sci. 2021, 11(8), 3621; https://0-doi-org.brum.beds.ac.uk/10.3390/app11083621 - 17 Apr 2021
Cited by 11 | Viewed by 2036
Abstract
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue Advances in Music Reading Systems)
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16 pages, 1163 KiB  
Article
A Deeper Look at Sheet Music Composer Classification Using Self-Supervised Pretraining
by Daniel Yang, Kevin Ji and TJ Tsai
Appl. Sci. 2021, 11(4), 1387; https://0-doi-org.brum.beds.ac.uk/10.3390/app11041387 - 04 Feb 2021
Cited by 4 | Viewed by 2651
Abstract
This article studies a composer style classification task based on raw sheet music images. While previous works on composer recognition have relied exclusively on supervised learning, we explore the use of self-supervised pretraining methods that have been recently developed for natural language processing. [...] Read more.
This article studies a composer style classification task based on raw sheet music images. While previous works on composer recognition have relied exclusively on supervised learning, we explore the use of self-supervised pretraining methods that have been recently developed for natural language processing. We first convert sheet music images to sequences of musical words, train a language model on a large set of unlabeled musical “sentences”, initialize a classifier with the pretrained language model weights, and then finetune the classifier on a small set of labeled data. We conduct extensive experiments on International Music Score Library Project (IMSLP) piano data using a range of modern language model architectures. We show that pretraining substantially improves classification performance and that Transformer-based architectures perform best. We also introduce two data augmentation strategies and present evidence that the model learns generalizable and semantically meaningful information. Full article
(This article belongs to the Special Issue Advances in Music Reading Systems)
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