Residual Recurrent CRNN for End-to-End Optical Music Recognition on Monophonic Scores

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.

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Data Augmentation for End-to-End Optical Music Recognition

10.1007/978-3-030-86198-8_5 ◽

2021 ◽

pp. 59-73

Author(s):

Juan C. López-Gutiérrez ◽

Jose J. Valero-Mas ◽

Francisco J. Castellanos ◽

Jorge Calvo-Zaragoza

Keyword(s):

Data Augmentation ◽

Optical Music Recognition ◽

Music Recognition ◽

End To End

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Approaching End-to-End Optical Music Recognition for Homophonic Scores

Pattern Recognition and Image Analysis - Lecture Notes in Computer Science ◽

10.1007/978-3-030-31321-0_13 ◽

2019 ◽

pp. 147-158

Author(s):

María Alfaro-Contreras ◽

Jorge Calvo-Zaragoza ◽

José M. Iñesta

Keyword(s):

Optical Music Recognition ◽

Music Recognition ◽

End To End

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Pengantar dan Survey Tentang Optical Music Recognition

Jurnal ULTIMATICS ◽

10.31937/ti.v6i1.331 ◽

2014 ◽

Vol 6 (1) ◽

pp. 36-39

Author(s):

Kevin Purwito

Keyword(s):

Character Recognition ◽

Optical Character Recognition ◽

Optical Music Recognition ◽

Optical Character ◽

Digital Format ◽

Music Recognition ◽

Index Terms ◽

The Many ◽

Further Development ◽

Music Symbol

This paper describes about one of the many extension of Optical Character Recognition (OCR), that is Optical Music Recognition (OMR). OMR is used to recognize musical sheets into digital format, such as MIDI or MusicXML. There are many musical symbols that usually used in musical sheets and therefore needs to be recognized by OMR, such as staff; treble, bass, alto and tenor clef; sharp, flat and natural; beams, staccato, staccatissimo, dynamic, tenuto, marcato, stopped note, harmonic and fermata; notes; rests; ties and slurs; and also mordent and turn. OMR usually has four main processes, namely Preprocessing, Music Symbol Recognition, Musical Notation Reconstruction and Final Representation Construction. Each of those four main processes uses different methods and algorithms and each of those processes still needs further development and research. There are already many application that uses OMR to date, but none gives the perfect result. Therefore, besides the development and research for each OMR process, there is also a need to a development and research for combined recognizer, that combines the results from different OMR application to increase the final result’s accuracy. Index Terms—Music, optical character recognition, optical music recognition, musical symbol, image processing, combined recognizer

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