Abstract
It is very common to find different versions of the same music work in archives of Opera Theaters. These differences correspond to modifications and annotations from the musicians. From the musicologist point of view, these variations are very interesting and deserve study. This paper explores the alignment of music scores as a tool for automatically detecting the passages that contain such differences. Given the difficulties in the recognition of handwritten music scores, our goal is to align the music scores and at the same time, avoid the recognition of music elements as much as possible. After removing the staff lines, braces and ties, the bar lines are detected. Then, the bar units are described as a whole using the Blurred Shape Model. The bar units alignment is performed by using Dynamic Time Warping. The analysis of the alignment path is used to detect the variations in the music scores. The method has been evaluated on a subset of the CVC-MUSCIMA dataset, showing encouraging results.
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References
Photoscore. http://www.neuratron.com/photoscore.htm
Sharpeye. http://www.visiv.co.uk/
Rebelo, A., Fujinaga, I., Paszkiewicz, F., Marcal, A., Guedes, C., Cardoso, J.: Optical music recognition: state-of-the-art and open issues. Int. J. Multimedia Inf. Retrieval 1(3), 173–190 (2012)
Fornés, A., Sánchez, G.: Analysis and recognition of music scores. In: Doermann, D., Tombre, K. (eds.) Handbook of Document Image Processing and Recognition, pp. 749–774. Springer, London (2014)
Rebelo, A., Capela, G., Cardoso, J.S.: Optical recognition of music symbols: a comparative study. Int. J. Doc. Anal. Recogn. 13(1), 19–31 (2010)
Fornés, A., Lladós, J., Sánchez, G., Karatzas, D.: Rotation invariant hand drawn symbol recognition based on a dynamic time warping model. Int. J. Doc. Anal. Recogn. 13(3), 229–241 (2010)
Miyao, H., Maruyama, M.: An online handwritten music symbol recognition system. IJDAR 9(1), 49–58 (2007)
Calvo-Zaragoza, J., Oncina, J.: Recognition of pen-based music notation with probabilistic machines. In: Proceedings of the 7th International Workshop on Machine Learning and Music, Barcelona, Spain (2014)
Myscript music. http://myscript.com/technology/music
Staffpad. http://www.staffpad.net/
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)
dos Santos Cardoso, J., Capela, A., Rebelo, A., Guedes, C., Pinto, K.: Staff detection with stable paths. IEEE Trans. Pattern Anal. Mach. Intell. 31(6), 1134–1139 (2009)
Visani, M., Kieu, V.C., Fornés, A., Journet, N.: ICDAR 2013 music scores competition: staff removal. In: 2013 12th International Conference on Document Analysis and Recognition (ICDAR), pp. 1407–1411. IEEE (2013)
Fornés, A., Dutta, A., Gordo, A., Lladós, J.: The 2012 music scores competitions: staff removal and writer identification. In: Kwon, Y.-B., Ogier, J.-M. (eds.) GREC 2011. LNCS, vol. 7423, pp. 173–186. Springer, Heidelberg (2013). doi:10.1007/978-3-642-36824-0_17
Escalera, S., Fornés, A., Pujol, O., Radeva, P., Sánchez, G., Lladós, J.: Blurred Shape Model for binary and grey-level symbol recognition. Pattern Recogn. Lett. 30(15), 1424–1433 (2009)
Sakoe, H., Chiba, S.: Dynamic programming algorithm optimization for spoken word recognition. IEEE Trans. Acoust. Speech Sig. Process. 26(1), 43–49 (1978)
Keogh, E., Ratanamahatana, C.: Exact indexing of dynamic time warping. Knowl. Inf. Syst. 7(3), 358–386 (2005)
Fornés, A., Dutta, A., Gordo, A., Lladós, J.: CVC-MUSCIMA: a ground truth of handwritten music score images for writer identification and staff removal. Int. J. Doc. Anal. Recogn. 15(3), 243–251 (2012)
Acknowledgment
This work has been partially supported by the Spanish project TIN2015-70924-C2-2-R, the European project ERC-2010-AdG-20100407-269796 and the Ramon y Cajal Fellowship RYC-2014-16831.
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Riba, P., Fornés, A., Lladós, J. (2017). Towards the Alignment of Handwritten Music Scores. In: Lamiroy, B., Dueire Lins, R. (eds) Graphic Recognition. Current Trends and Challenges. GREC 2015. Lecture Notes in Computer Science(), vol 9657. Springer, Cham. https://doi.org/10.1007/978-3-319-52159-6_8
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