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Optical Music Recognition as the Case of Imbalanced Pattern Recognition: A Study of Single Classifiers

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Knowledge, Information and Creativity Support Systems: Recent Trends, Advances and Solutions

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 364))

Abstract

The article is focused on a particular aspect of classification, namely the imbalance of recognized classes. The paper contains a comparative study of results of musical symbols classification using known algorithms: k-nearest neighbors, k-means, Mahalanobis minimal distance, and decision trees. Authors aim at addressing the problem of imbalanced pattern recognition. First, we theoretically analyze difficulties entailed in the classification of music notation symbols. Second, in the enclosed case study we investigate the fitness of named single classifiers on real data. Conducted experiments are based on own implementations of named algorithms with all necessary image processing tasks. Results are highly satisfying.

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Acknowledgments

The research is supported by the National Science Center, grant No 2011/01/B/ST6/06478, decision no DEC-2011/01/B/ST6/06478.

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Authors and Affiliations

  1. Faculty of Mathematics and Information Science, Warsaw University of Technology, Plac Politechniki 1, 00-660, Warsaw, Poland

    Agnieszka Jastrzebska & Wojciech Lesinski

  2. Faculty of Mathematics and Computer Science, University of Bialystok, ul. Sosnowa 64, 15-887, Bialystok, Poland

    Wojciech Lesinski

Authors
  1. Agnieszka Jastrzebska
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  2. Wojciech Lesinski
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Corresponding author

Correspondence to Wojciech Lesinski .

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Editors and Affiliations

  1. Department of Automatic Control and Biomedical Engineering, AGH University of Science and Technology, Kraków, Poland, and International Centre for Decision Sciences and Forecasting, Kraków, Poland

    Andrzej M.J. Skulimowski

  2. Polish Academy of Sciences, Systems Research Institute, Warszawa, Poland, and AGH University of Science and Technology, Kraków, Poland

    Janusz Kacprzyk

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Jastrzebska, A., Lesinski, W. (2016). Optical Music Recognition as the Case of Imbalanced Pattern Recognition: A Study of Single Classifiers. In: Skulimowski, A., Kacprzyk, J. (eds) Knowledge, Information and Creativity Support Systems: Recent Trends, Advances and Solutions. Advances in Intelligent Systems and Computing, vol 364. Springer, Cham. https://doi.org/10.1007/978-3-319-19090-7_37

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  • DOI: https://doi.org/10.1007/978-3-319-19090-7_37

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-19089-1

  • Online ISBN: 978-3-319-19090-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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