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Staff-Line Detection on Grayscale Images with Pixel Classification

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10255)

Abstract

Staff-line detection and removal are important processing steps in most Optical Music Recognition systems. Traditional methods make use of heuristic strategies based on image processing techniques with binary images. However, binarization is a complex process for which it is difficult to achieve perfect results. In this paper we describe a novel staff-line detection and removal method that deals with grayscale images directly. Our approach uses supervised learning to classify each pixel of the image as symbol, staff, or background. This classification is achieved by means of Convolutional Neural Networks. The features of each pixel consist of a square window from the input image centered at the pixel to be classified. As a case of study, we performed experiments with the CVC-Muscima dataset. Our approach showed promising performance, outperforming state-of-the-art algorithms for staff-line removal.

Keywords

Music recognition Staff-line removal Grayscale domain 

Notes

Acknowledgements

This work has been supported by the Social Sciences and Humanities Research Council of Canada and the Spanish Ministerio de Educación, Cultura y Deporte through a FPU Fellowship (Ref. AP2012–0939).

References

  1. 1.
    Typke, R., Wiering, F., Veltkamp, R.C.: A survey of music information retrieval systems. In: Proceedings of the 6th International Conference on Music Information Retrieval, London, UK, pp. 153–160 (2005)Google Scholar
  2. 2.
    Bainbridge, D., Bell, T.: The challenge of optical music recognition. Comput. Humanit. 35(2), 95–121 (2001)CrossRefGoogle Scholar
  3. 3.
    Rebelo, A., Capela, G., Cardoso, J.S.: Optical recognition of music symbols. Int. J. Doc. Anal. Recogn. (IJDAR) 13(1), 19–31 (2010)CrossRefGoogle Scholar
  4. 4.
    Calvo-Zaragoza, J., Oncina, J.: Recognition of pen-based music notation: the HOMUS dataset. In: 22nd International Conference on Pattern Recognition (ICPR), Stockholm, Sweden, pp. 3038–3043 (2014)Google Scholar
  5. 5.
    Pugin, L.: Optical music recognition of early typographic prints using hidden Markov models. In: Proceedings of the 7th International Conference on Music Information Retrieval, pp. 53–56 (2006)Google Scholar
  6. 6.
    Calvo-Zaragoza, J., Barbancho, I., Tardón, L.J., Barbancho, A.M.: Avoiding staff removal stage in optical music recognition: application to scores written in white mensural notation. Pattern Anal. Appl. 18(4), 933–943 (2015)MathSciNetCrossRefGoogle Scholar
  7. 7.
    Rebelo, A., Fujinaga, I., Paszkiewicz, F., Marçal, A.R.S., Guedes, C., Cardoso, J.S.: Optical music recognition: state-of-the-art and open issues. Int. J. Multimedia Inf. Retr. (IJMIR) 1(3), 173–190 (2012)CrossRefGoogle Scholar
  8. 8.
    Dalitz, C., Droettboom, M., Pranzas, B., Fujinaga, I.: A comparative study of staff removal algorithms. IEEE Trans. Pattern Anal. Mach. Intell. 30(5), 753–766 (2008)CrossRefGoogle Scholar
  9. 9.
    Dos Santos Cardoso, J., Capela, A., Rebelo, A., Guedes, C., Pinto da Costa, J.: Staff detection with stable paths. IEEE Trans. Pattern Anal. Mach. Intell. 31(6), 1134–1139 (2009)CrossRefGoogle Scholar
  10. 10.
    Rebelo, A., Cardoso, J.: Staff line detection and removal in the grayscale domain. In: Proceedings of the 12th International Conference on Document Analysis and Recognition (ICDAR), pp. 57–61, August 2013Google Scholar
  11. 11.
    Dutta, A., Pal, U., Fornés, A., Llados, J.: An efficient staff removal approach from printed musical documents. In: Proceedings of the 20th International Conference on Pattern Recognition (ICPR), pp. 1965–1968, August 2010Google Scholar
  12. 12.
    Piątkowska, W., Nowak, L., Pawłowski, M., Ogorzałek, M.: Stafflines pattern detection using the swarm intelligence algorithm. In: Bolc, L., Tadeusiewicz, R., Chmielewski, L.J., Wojciechowski, K. (eds.) ICCVG 2012. LNCS, vol. 7594, pp. 557–564. Springer, Heidelberg (2012). doi: 10.1007/978-3-642-33564-8_67 CrossRefGoogle Scholar
  13. 13.
    Su, B., Lu, S., Pal, U., Tan, C.: An effective staff detection and removal technique for musical documents. In: Proceedings of the 10th IAPR International Workshop on Document Analysis Systems (DAS), pp. 160–164 (2012)Google Scholar
  14. 14.
    Géraud, T.: A morphological method for music score staff removal. In: Proceedings of the 21st International Conference on Image Processing (ICIP), Paris, France, pp. 2599–2603 (2014)Google Scholar
  15. 15.
    dos Santos Montagner, I., Hirata, R., Hirata, N.S.: A machine learning based method for staff removal. In: Proceedings of the 22nd International Conference on Pattern Recognition (ICPR), pp. 3162–3167 (2014)Google Scholar
  16. 16.
    Calvo-Zaragoza, J., Micó, L., Oncina, J.: Music staff removal with supervised pixel classification. IJDAR 19(3), 211–219 (2016)CrossRefGoogle Scholar
  17. 17.
    LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436–444 (2015)CrossRefGoogle Scholar
  18. 18.
    Zeiler, M.D.: ADADELTA: an adaptive learning rate method. CoRR abs/1212.5701 (2012)Google Scholar
  19. 19.
    Fornés, A., Kieu, V.C., Visani, M., Journet, N., Dutta, A.: The ICDAR/GREC 2013 music scores competition: staff removal. In: Proceedings of the 10th International Workshop on Graphics Recognition, Current Trends and Challenges GREC, Revised Selected Papers, Bethlehem, PA, USA, pp. 207–220 (2013)Google Scholar
  20. 20.
    Visaniy, M., Kieu, V., Fornés, A., Journet, N.: ICDAR/GREC 2013 music scores competition: staff removal. In: Proceedings of the 12th International Conference on Document Analysis and Recognition (ICDAR), pp. 1407–1411 (2013)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Pattern Recognition and Artificial Intelligence GroupUniversity of AlicanteAlicanteSpain
  2. 2.Schulich School of Music, CIRMMTMcGill UniversityMontréalCanada

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