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Applying Machine Learning in Optical Music Recognition of Numbered Music Notation

Applying Machine Learning in Optical Music Recognition of Numbered Music Notation

Fu-Hai Frank Wu
Copyright: © 2017 |Volume: 8 |Issue: 3 |Pages: 21
ISSN: 1947-8534|EISSN: 1947-8542|EISBN13: 9781522512493|DOI: 10.4018/IJMDEM.2017070102
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MLA

Wu, Fu-Hai Frank. "Applying Machine Learning in Optical Music Recognition of Numbered Music Notation." IJMDEM vol.8, no.3 2017: pp.21-41. http://doi.org/10.4018/IJMDEM.2017070102

APA

Wu, F. F. (2017). Applying Machine Learning in Optical Music Recognition of Numbered Music Notation. International Journal of Multimedia Data Engineering and Management (IJMDEM), 8(3), 21-41. http://doi.org/10.4018/IJMDEM.2017070102

Chicago

Wu, Fu-Hai Frank. "Applying Machine Learning in Optical Music Recognition of Numbered Music Notation," International Journal of Multimedia Data Engineering and Management (IJMDEM) 8, no.3: 21-41. http://doi.org/10.4018/IJMDEM.2017070102

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Abstract

Although research of optical music recognition (OMR) has existed for few decades, most of efforts were put in step of image processing to approach upmost accuracy and evaluations were not in common ground. And major music notations explored were the conventional western music notations with staff. On contrary, the authors explore the challenges of numbered music notation, which is popular in Asia and used in daily life for sight reading. The authors use different way to improve recognition accuracy by applying elementary image processing with rough tuning and supplementing with methods of machine learning. The major contributions of this work are the architecture of machine learning specified for this task, the dataset, and the evaluation metrics, which indicate the performance of OMR system, provide objective function for machine learning and highlight the challenges of the scores of music with the specified notation.

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