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Virtual Music Teacher for New Music Learners with Optical Music Recognition

  • Viet-Khoi PhamEmail author
  • Hai-Dang Nguyen
  • Minh-Triet Tran
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9192)

Abstract

Learn to read and understand a music sheet, then play it on a musical instrument are difficult tasks to most beginner music learners. This motivates the authors to propose Virtual Music Teacher, a system to assist beginner music learners in their learning process. By applying our proposed lightweight Optical Music Recognition algorithm to scan and recognize a music sheet, then combine with sound classifying technique, the proposed system can learn what note to be played next, then help a music learner to play it correctly. The experimental results on the dataset consisting of 15 musical scores for beginners show that the proposed system can classify with precision up to 99.9 % using multiple SVM classifiers approach, whereas the sound classifying technique using Fast Fourier Transform can classify note’s pitch recorded from a piano with precision up to 95.71 %. The system is implemented as an application on mobile devices and can be used to assist a music learner to play not only piano but other musical instruments as well.

Keywords

Optical music recognition Note’s pitch recognition Virtual music teacher 

References

  1. 1.
    Optical Music Recognition Bibliography, a list of works done on OMR. http://ddmal.music.mcgill.ca/wiki/Optical_Music_Recognition_Bibliography Accessed on 25 February 2014
  2. 2.
    Pham, V.K, Nguyen, H.D., Nguyen-Khac, T.A., Tran, M.T.: Apply Lightweight Recognition Algorithms in Optical Music Recognition. In: Seventh International Conference on Machine Vision (ICMV 2014). Proceedings of SPIE vol. 9445 (2015)Google Scholar
  3. 3.
    Pruslin, D.: Automatic recognition of sheet music. PhD thesis, Massachusetts Institute of Technology (1966)Google Scholar
  4. 4.
    Prerau, D.: Computer pattern recognition of standard engraved music notation. PhD thesis, Massachusetts Institute of Technology (1970)Google Scholar
  5. 5.
    Byrd, D.: Optical Music Recognition Systems survey. Indiana University (rev, School of Informatics and School of Music (2007)Google Scholar
  6. 6.
    Rossant, F., Bloch, I.: Robust and adaptive OMR system including fuzzy modeling, fusion of musical rules, and possible error detection. EURASIP J. Appl. Sig. Process. 2007(1), 160 (2007)zbMATHGoogle Scholar
  7. 7.
    Toyama, F., Shoji, K., Miyamichi, J.: Symbol recognition of printed piano scores with touching symbols. In: Proceedings of the International Conference on Pattern Recognition, pp. 480–483 (2006)Google Scholar
  8. 8.
    Pugin, L.: Optical music recognition of early typographic prints using Hidden Markov Models. In: Proceedings of the International Society for Music Information Retrieval, pp. 53–56 (2006)Google Scholar
  9. 9.
    Rebelo, A., Capela, G., Cardoso, J.S.: Optical recognition of music symbols: A comparative study. Int. J. Doc. Anal. Recogn. 13, 19–31 (2010)CrossRefGoogle Scholar
  10. 10.
    Rebelo, A., Fujinaga, I., Paszkiewicz, F., Marcal, A.R.S., Guedes, C., Cardoso, J.S.: Optical music recognition: state-of the-art and open issues. Int. J. Multimedia Inf. Retrieval 1(3), 173–190 (2012)CrossRefGoogle Scholar
  11. 11.
    Rebelo, A.: New methodologies towards an automatic optical recognition of handwritten musical scores. Master of Science thesis, University of Porto, Portugal (2008)Google Scholar
  12. 12.
    Otsu, N.: A Threshold Selection Method from Gray-Level Histograms. IEEE Trans. Syst. Man Cybern. 9(1), 62–66 (1979)MathSciNetCrossRefGoogle Scholar
  13. 13.
    Duda, R.O., Hart, P.E.: Use of the Hough Transformation to Detect Lines and Curves in Pictures. Commun. ACM 15(1), 11–15 (1972)CrossRefzbMATHGoogle Scholar
  14. 14.
    Marchand, S.: An efficient pitch-tracking algorithm using a combination of Fourier Transforms. In: Proceedings of the Conference on Digital Audio Effects (DAFX 2001), pp. 170–174 (2001)Google Scholar
  15. 15.
    Frequency of pitches: http://www.seventhstring.com/resources/notefrequencies.html Accessed on 8 March 2015
  16. 16.
    Synthetic Score Database. http://gamera.informatik.hsnr.de/addons/musicstaves/testsetmusicstaves.tar.gz Accessed on 25 February 2014

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Viet-Khoi Pham
    • 1
    Email author
  • Hai-Dang Nguyen
    • 1
  • Minh-Triet Tran
    • 1
  1. 1.Faculty of Information TechnologyUniversity of Science, VNU-HCMHồ Chí MinhVietnam

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