Methods of Artificial Intelligence in Blind People Education

  • Bohdan Macukow
  • Wladyslaw Homenda
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4029)


This paper presents the idea of recognition of music symbols to help the blind people reading music scores and operating music notation. The discussion is focused on two main topics. The first topic is the concept of the computer program, which recognizes music notation and processes music information while the second is a brief presentation of music processing methods including recognition of music notation – Optical Music Recognition technology – based on artificial neural networks. The short description and comparison of effectiveness of artificial neural networks is also given.


Automatic Recognition Blind People Music Notation Blind User Notation Editor 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Bainbridge, D., Bell, T.: The challenge of optical music recognition. Computers and the Humanities 35, 95–121 (2001)CrossRefGoogle Scholar
  2. 2.
    Carter, N.P., Bacon, R.A.: Automatic Recognition of Printed Music. In: Baird, H.S., Bunke, H., Yamamoto, K. (eds.) Structured Document Analysis, Analysis, pp. 456–465. Springer, Heidelberg (1992)Google Scholar
  3. 3.
    Dannenberg, R.: Music Representation Issues, Techniques, and Systems. Computer Music Journal 17(3), 20–30 (1993)CrossRefGoogle Scholar
  4. 4.
    Fujinaga, I.: Adaptive optical music recognition. In: 16th Inter. Congress of the Inter. Musicological Society, Oxford University Press, Oxford (2001)Google Scholar
  5. 5.
    Homenda, W.: Automatic recognition of printed music and its conversion into playable music data. Control and Cybernetics 25(2), 353–367 (1996)MATHGoogle Scholar
  6. 6.
    Homenda, W.: Granular Computing as an Abstraction of Data Aggregation - a View on Optical Music Recognition. Archives of Control Sciences 12 (4), 433–455 (2002)MATHGoogle Scholar
  7. 7.
    Homenda, W.: Optical Music Recognition: the Case Study of Pattern Recognition. In: Kurzyski, et al. (eds.) Computer Recognition Systems, pp. 835–842. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  8. 8.
    Homenda, W., Luckner, M.: Automatic Recognition of Music Notation Using Neural Networks. In: Proc. of the International Conference On Artificial Intelligence and Systems, Div-nomorskoye, Russia, September 3-10 (2004)Google Scholar
  9. 9.
    Homenda, W., Mossakowski, K.: Music Symbol Recognition: Neural Networks vs. Statistical Methods. In: EUROFUSE Workshop On Data And Knowledge Engineering, Warsaw, Poland, September 22–25, pp. 265–271 (2004)Google Scholar
  10. 10.
    Krolick, B.: How to Read Braille Music, 2nd edn. Opus Technologies (1998)Google Scholar
  11. 11.
    McPherson, J.R.: Introducing feedback into an optical music recognition system. In: Third Internat. Conf. on Music Information Retrieval, Paris, France (2002)Google Scholar
  12. 12.
    Moniuszko, T.: Design and implementation of music processing computer program for blind people (in Polish), Master Thesis, Warsaw University of Technology, Warsaw (2006)Google Scholar
  13. 13.
    Pruslin, D.H.: Automatic Recognition of Sheet Music, PhD Thesis, Massachusetts Institute of Technology (1966)Google Scholar
  14. 14.
    MIDI 1.0, Detailed Specification, Document version 4.1.1 (February 1990)Google Scholar
  15. 15.
  16. 16.
  17. 17.
  18. 18.

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Bohdan Macukow
    • 1
  • Wladyslaw Homenda
    • 1
  1. 1.Faculty of Mathematics and Information ScienceWarsaw University of TechnologyWarszawaPoland

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