International Journal of Contents

  • 제12권4호
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  • Pages.53-61
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  • 2016
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  • 1738-6764(pISSN)
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  • 2093-7504(eISSN)
한국콘텐츠학회 (The Korea Contents Association)
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Improved Lexicon-driven based Chord Symbol Recognition in Musical Images

  • Dinh, Cong Minh (Department of Computer Engineering Chonnam National University) ;
  • Do, Luu Ngoc (Department of Computer Engineering Chonnam National University) ;
  • Yang, Hyung-Jeong (Department of Computer Engineering Chonnam National University) ;
  • Kim, Soo-Hyung (Department of Computer Engineering Chonnam National University) ;
  • Lee, Guee-Sang (Department of Computer Engineering Chonnam National University)
  • 투고 : 2016.09.06
  • 심사 : 2016.12.22
  • 발행 : 2016.12.28
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초록

Although extensively developed, optical music recognition systems have mostly focused on musical symbols (notes, rests, etc.), while disregarding the chord symbols. The process becomes difficult when the images are distorted or slurred, although this can be resolved using optical character recognition systems. Moreover, the appearance of outliers (lyrics, dynamics, etc.) increases the complexity of the chord recognition. Therefore, we propose a new approach addressing these issues. After binarization, un-distortion, and stave and lyric removal of a musical image, a rule-based method is applied to detect the potential regions of chord symbols. Next, a lexicon-driven approach is used to optimally and simultaneously separate and recognize characters. The score that is returned from the recognition process is used to detect the outliers. The effectiveness of our system is demonstrated through impressive accuracy of experimental results on two datasets having a variety of resolutions.

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참고문헌

  1. L. Pugin, "Optical Music Recognition of Early Typographic Prints using Hidden Markov Models," Proceedings of the 7th International Conference on Music Information Retrieval, Oct. 2006, pp.53-56.
  2. J. C. Pinto, P. Vieira, M. Ramalho, M. Mengucci, P. Pina, and F. Muge, "Ancient Music Recovery for Digital Libraries," in Research and Advanced Technology for Digital Libraries," J. Borbinha and T. Baker, eds. Springer Berlin Heidelberg, vol. 1923, 2000, pp. 24-34.
  3. E. Borovikov, A survey of modern optical character recognition techniques, 2014.
  4. V. K. Govindan and A. P. Shivaprasad, "Character recognition - A review," Pattern Recognition., vol. 23, no. 7, 1990, pp. 671-683. https://doi.org/10.1016/0031-3203(90)90091-X
  5. E. Kodirov, S. Han, G. S. Lee, and Y. C. Kim, "Music with Harmony: Chord Separation and Recognition in Printed Music Score Images," Proceedings of the 8th International Conference on Ubiquitous Information Management and Communication, 2014, pp. 1-8.
  6. S. H. Kim, S. Jeong, and C. Y. Suen, "A lexicon-driven approach for optimal segment combination in off-line recognition of unconstrained handwritten Korean words," Pattern Recognit., vol. 34, no. 7, 2001, pp. 1437-1447. https://doi.org/10.1016/S0031-3203(00)00098-4
  7. Q. N. Vo, T. Nguyen, S. H. Kim, H. J. Yang, and G. S. Lee, "Distorted Music Score Recognition without Staff line Removal," Pattern Recognition (ICPR), 2014 22nd International Conference on, Aug. 2014, pp. 2956-2960.
  8. B. Su, S. Lu, U. Pal, and C. L. Tan, "An Effective Staff Detection and Removal Technique for Musical Documents," Document Analysis Systems (DAS), 2012 10th IAPR International Workshop on, Mar. 2012, pp. 160-164.
  9. J. A. Burgoyne, Y. Ouyang, T. Himmelman, J. Devaney, L. Pugin, and I. Fujinaga, "Lyric extraction and recognition on digital images of early music sources," Proceedings of the 10th International Society for Music Information Retrieval Conference, vol.10, 2009, pp. 723-727.
  10. M. Feldbach and K. D. Tonnies, "Line detection and segmentation in historical church registers," Document Analysis and Recognition, 2001 Proceedings, Sixth International Conference on, 2001, pp. 743-747.
  11. R. Gorow, Hearing and Writing Music: Professional Training for Today's Musician, 2nd ed., September Publishing, 2000.
  12. Q. H. Wang, L. S. Lopes, and D. M. J. Tax, "Visual Object Recognition Through One-Class Learning," in Image Analysis and Recognition, Springer Berlin Heidelberg, vol. 3211, 2004, pp. 463-470.
  13. P. Juszczak, D. M. J. Tax, E. Pekalska, and R. P. W. Duin, "Minimum spanning tree based one-class classifier," Neurocomputing, vol. 72, no. 7-9, 2009, pp. 1859-1869. https://doi.org/10.1016/j.neucom.2008.05.003
  14. D. M. J. Tax, One-class Classification, 2001.
  15. D. M. J. Tax and R. P. W. Duin, "Support vector domain description," Pattern Recognition. Letter, vol. 20, 1999, p. 1191. https://doi.org/10.1016/S0167-8655(99)00087-2
  16. E. Pekalska, D. M. J. Tax, and R. P. W. Duin, One-Class LP Classifiers for Dissimilarity Representations, in Advances in Neural Information Processing Systems 15, MIT Press, 2003, pp. 777-784.
  17. G. R. G. Lanckriet, L. E. Ghaoui, and M. I. Jordan, Robust Novelty Detection with Single-Class MPM, in Advances in Neural Information Processing Systems 15, MIT Press, 2003, pp. 929-936.
  18. T. Fawcett, "An Introduction to ROC Analysis," Pattern Recognition. Letter, vol. 27, 2006, pp. 861-874. https://doi.org/10.1016/j.patrec.2005.10.010
  19. A. P. Bradley, "The use of the area under the ROC curve in the evaluation of machine learning algorithms," Pattern Recognit., vol. 30, no. 7, 1997, pp. 1145-1159. https://doi.org/10.1016/S0031-3203(96)00142-2
  20. T. Nartker, K. Taghva, R. Young, J. Borsack, and A. Condit, "OCR correction based on document level knowledge," International Symposium on Electronic Imaging Science and Technology, vol. 5010, 2003, pp. 103-110.
  21. S. B. Needleman and C. D. Wunsch, "A general method applicable to the search for similarities in the amino acid sequence of two proteins," J. Mol. Biol., vol. 48, no. 3, Mar. 1970, pp. 443-453. https://doi.org/10.1016/0022-2836(70)90057-4
  22. A. B. David, "Comparison of classification accuracy using Cohen's weighted kappa," Expert Syst. Appl., vol. 34, 2008, pp. 825-832. https://doi.org/10.1016/j.eswa.2006.10.022