Recognition of online handwritten music symbols

Paper submitted to MML 2013, 6th International Workshop on Machine Learning and Music, Prague, September 23, 2013.An effective way of digitizing a new musical composition is to use an e-pen and tablet application in which the user's pen strokes are recognized online and the digital score is created with the sole effort of the composition itself. This work aims to be a starting point for research on the recognition of online handwritten music notation. To this end, different alternatives within the two modalities of recognition resulting from this data are presented: online recognition, which uses the strokes marked by a pen, and offline recognition, which uses the image generated after drawing the symbol. A comparative experiment with common machine learning algorithms over a dataset of 3800 samples and 32 different music symbols is presented. Results show that samples of the actual user are needed if good classification rates are pursued. Moreover, algorithms using the online data, on average, achieve better classification results than the others

Decoupling music notation to improve end-to-end Optical Music Recognition

Inspired by the Text Recognition field, end-to-end schemes based on Convolutional Recurrent Neural Networks (CRNN) trained with the Connectionist Temporal Classification (CTC) loss function are considered one of the current state-of-the-art techniques for staff-level Optical Music Recognition (OMR). Unlike text symbols, music-notation elements may be defined as a combination of (i) a shape primitive located in (ii) a certain position in a staff. However, this double nature is generally neglected in the learning process, as each combination is treated as a single token. In this work, we study whether exploiting such particularity of music notation actually benefits the recognition performance and, if so, which approach is the most appropriate. For that, we thoroughly review existing specific approaches that explore this premise and propose different combinations of them. Furthermore, considering the limitations observed in such approaches, a novel decoding strategy specifically designed for OMR is proposed. The results obtained with four different corpora of historical manuscripts show the relevance of leveraging this double nature of music notation since it outperforms the standard approaches where it is ignored. In addition, the proposed decoding leads to significant reductions in the error rates with respect to the other cases.This paper is part of the project I+D+i PID2020-118447RA-I00 (MultiScore), funded by MCIN/AEI/10.13039/501100011033. The first author is supported by grant FPU19/04957 from the Spanish Ministerio de Universidades. The second author is supported by grant ACIF/2021/356 from “Programa I+D+i de la Generalitat Valenciana“. The third author is supported by grant APOSTD/2020/256 from “Programa I+D+i de la Generalitat Valenciana”

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

[EN] 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 Images of Music Staves (PrIMuS) 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.This work was supported by the Social Sciences and Humanities Research Council of Canada, and the Spanish Ministerio de Economia y Competitividad through Project HISPAMUS Ref. No. TIN2017-86576-R (supported by UE FEDER funds).Calvo-Zaragoza, J.; Rizo, D. (2018). End-to-End Neural Optical Music Recognition of Monophonic Scores. Applied Sciences. 8(4). https://doi.org/10.3390/app8040606S8

Exploiting the Two-Dimensional Nature of Agnostic Music Notation for Neural Optical Music Recognition

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.This research work was partially funded by the University of Alicante through project GRE19-04, by the “Programa I+D+i de la Generalitat Valenciana” through grant APOSTD/2020/256, and by the Spanish Ministerio de Universidades through grant FPU19/04957

Recognition of handwritten music scores

The recognition of handwritten music scores still remains an open problem. The existing approaches can only deal with very simple handwritten scores mainly because of the variability in the handwriting style and the variability in the composition of groups of music notes (i.e. compound music notes). In this work on the one hand I study the isolated symbols (i.e half-note, quarter-note, clefs, sharps) and on the other hand the compound music notes. Firstly, I will separate the isolated symbols (i.e half-notes, quarter-notes, clefs, sharps) to the compounds and I will study each one separately. The isolated symbols will be recognized with symbol recognition methods and compounds with a primitive hierarchy and syntactic rules. The method has been tested using several handwritten music scores of the CVC-MUSCIMA database and compared with a commercial Optical Music Recognition software. Given that my method is learning-free, the obtained results are promising.El reconeixement de partitures musicals manuscrites segueix sent un problema obert. Els enfocaments existents només poden reconéixer partitures manuscrites molt simples, principalment a causa de la variabilitat en l'estil d'escriptura i la variabilitat en la composició dels grups de notes musicals (p.e. els símbols musicals compostos). En aquest treball, per començar, se separaran els símbols simples (p.e blanques, negres, claus, sostinguts) dels compostos i els estudiaré per separat. Els símbols simples mitjançant mètodes de reconeixement de símbols i els compostos a partir d'una jerarquia de primitives i regles sintàctiques. El meu mètode ha estat provat utilitzant diferents partitures de música escrita a mà de la base de dades CVC-MUSCIMA i comparat amb un programari de reconeixement òptic musical comercial. Tenint en compte que el meu mètode és d'aprenentatge lliure, els resultats obtinguts són prometedors.El reconocimiento de partituras musicales manuscritas sigue siendo un problema abierto. Los enfoques existentes sólo pueden reconocer partituras manuscritas muy simples, principalmente debido a la variabilidad en el estilo de escritura y la variabilidad en la composición de los grupos de notas musicales (p.e. los símbolos musicales compuestos). En este trabajo, para empezar, se separarán los símbolos simples (p.e blancas, negras, llaves, sostenidos) de los compuestos y los estudiaré por separado. Los símbolos simples mediante métodos de reconocimiento de símbolos y los compuestos a partir de una jerarquía de primitivas y reglas sintácticas. Mi método ha sido probado utilizando diferentes partituras de música escrita a mano de la base de datos CVC-MUSCIMA y comparado con un software de reconocimiento óptico musical comercial. Teniendo en cuenta que mi método es de aprendizaje libre, los resultados obtenidos son prometedores

Optical Music Recognition: State of the Art and Major Challenges

Optical Music Recognition (OMR) is concerned with transcribing sheet music into a machine-readable format. The transcribed copy should allow musicians to compose, play and edit music by taking a picture of a music sheet. Complete transcription of sheet music would also enable more efficient archival. OMR facilitates examining sheet music statistically or searching for patterns of notations, thus helping use cases in digital musicology too. Recently, there has been a shift in OMR from using conventional computer vision techniques towards a deep learning approach. In this paper, we review relevant works in OMR, including fundamental methods and significant outcomes, and highlight different stages of the OMR pipeline. These stages often lack standard input and output representation and standardised evaluation. Therefore, comparing different approaches and evaluating the impact of different processing methods can become rather complex. This paper provides recommendations for future work, addressing some of the highlighted issues and represents a position in furthering this important field of research

Proceedings of the 4th International Workshop on Reading Music Systems

The International Workshop on Reading Music Systems (WoRMS) is a workshop that tries to connect researchers who develop systems for reading music, such as in the field of Optical Music Recognition, with other researchers and practitioners that could benefit from such systems, like librarians or musicologists. The relevant topics of interest for the workshop include, but are not limited to: Music reading systems; Optical music recognition; Datasets and performance evaluation; Image processing on music scores; Writer identification; Authoring, editing, storing and presentation systems for music scores; Multi-modal systems; Novel input-methods for music to produce written music; Web-based Music Information Retrieval services; Applications and projects; Use-cases related to written music. These are the proceedings of the 4th International Workshop on Reading Music Systems, held online on Nov. 18th 2022.Comment: Proceedings edited by Jorge Calvo-Zaragoza, Alexander Pacha and Elona Shatr