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Optical Music Recognition: Architecture and Algorithms

Optical Music Recognition: Architecture and Algorithms

Pierfrancesco Bellini, Ivan Bruno, Paolo Nesi
Copyright: © 2008 |Pages: 31
ISBN13: 9781599041506|ISBN10: 1599041502|EISBN13: 9781599041520
DOI: 10.4018/978-1-59904-150-6.ch005
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MLA

Bellini, Pierfrancesco, et al. "Optical Music Recognition: Architecture and Algorithms." Interactive Multimedia Music Technologies, edited by Kia Ng and Paolo Nesi, IGI Global, 2008, pp. 80-110. https://doi.org/10.4018/978-1-59904-150-6.ch005

APA

Bellini, P., Bruno, I., & Nesi, P. (2008). Optical Music Recognition: Architecture and Algorithms. In K. Ng & P. Nesi (Eds.), Interactive Multimedia Music Technologies (pp. 80-110). IGI Global. https://doi.org/10.4018/978-1-59904-150-6.ch005

Chicago

Bellini, Pierfrancesco, Ivan Bruno, and Paolo Nesi. "Optical Music Recognition: Architecture and Algorithms." In Interactive Multimedia Music Technologies, edited by Kia Ng and Paolo Nesi, 80-110. Hershey, PA: IGI Global, 2008. https://doi.org/10.4018/978-1-59904-150-6.ch005

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Abstract

Optical music recognition is a key problem for coding western music sheets in the digital world. This problem has been addressed in several manners obtaining suitable results only when simple music constructs are processed. To this end, several different strategies have been followed, to pass from the simple music sheet image to a complete and consistent representation of music notation symbols (symbolic music notation or representation). Typically, image processing, pattern recognition and symbolic reconstruction are the technologies that have to be considered and applied in several manners the architecture of the so called OMR (Optical Music Recognition) systems. In this chapter, the O3MR (Object Oriented Optical Music Recognition) system is presented. It allows producing from the image of a music sheet the symbolic representation and save it in XML format (WEDELMUSIC XML and MUSICXML). The algorithms used in this process are those of the image processing, image segmentation, neural network pattern recognition, and symbolic reconstruction and reasoning. Most of the solutions can be applied in other field of image understanding. The development of the O3MR solution with all its algorithms has been partially supported by the European Commission, in the IMUTUS Research and Development project, while the related music notation editor has been partially funded by the research and development WEDELMUSIC project of the European Commission. The paper also includes a methodology for the assessment of other OMR systems. The set of metrics proposed has been used to assess the quality of results produce by the O3MR with respect the best OMR on market.

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