Abstract
Optical Music Recognition (OMR) is a process that employs computer science techniques to musical scores recognition. This paper presents the development framework of “Copista”, an OMR system proposed to recognize handwritten scores especially regarding a historical music collection. “Copista” is the Brazilian word for Scribe, someone who writes music scores. The proposed system is useful to music collection preservation and supporting further research and development of OMR systems.
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References
Ariza, C., Cuthbert, M.: The music21 Stream: A New Object Model for Representing, Filtering, and Transforming Symbolic Musical Structures. MPublishing, University of Michigan Library, Ann Arbor (2011)
MIDI Manufacturers Association: The complete MIDI 1.0 detailed specification: incorporating all recommended practices. MIDI Manufacturers Association (1996)
Avidan, S., Shamir, A.: Seam carving for content-aware image resizing. In: ACM Transactions on Graphics (TOG), vol. 26, p. 10. ACM (2007)
Bainbridge, D., Bell, T.: Dealing with superimposed objects in optical music recognition (1997)
Bainbridge, D., Bell, T.: The challenge of optical music recognition. Comput. Hum. 35(2), 95–121 (2001)
Buxton, W., Reeves, W., Baecker, R., Mezei, L.: The use of hierarchy and instance in a data structure for computer music. Comput. Music J. 10–20 (1978)
Cuthbert, M.S., Ariza, C.: Music21: a toolkit for computer-aided musicology and symbolic music data (2010)
Dori, D., Doerman, D., Shin, C., Haralick, R., Phillips, I., Buchman, M., Ross, D.: Handbook on optical character recognition and document image analysis, chapter the representation of document structure: a generic object-process analysis (1996)
Fujinaga, I.: Staff detection and removal. In: Visual Perception of Music Notation: On-line and Off-line Recognition, pp. 1–39 (2004)
Fukunaga, K.: Introduction to Statistical Pattern Recognition. Academic Press, Cambridge (2013)
Furht, B.: Handbook of Augmented Reality. Springer, Heidelberg (2011)
Gonzalez, R.C., Woods, R.E., Eddins, S.L.: Digital Image Processing Using MATLAB. Pearson Education India, London (2004)
Good, M.: Musicxml for notation and analysis. Virtual Score: Represent. Retr. Restor. 12, 113–124 (2001)
Grande, C.: The notation interchange file format: a windows-compliant approach. In: Beyond MIDI, pp. 491–512. MIT Press (1997)
Hoos, H.H., Hamel, K.A., Renz, K., Kilian, J.: The guido notation format - a novel approach for adequately representing score-level music (1998)
Koendrik, J.J.: Computational vision (book). Ecol. Psychol. 4(2), 121–128 (1992)
Laia, M.A.d.M.: Filtragem de Kalman não linear com redes neurais embarcada em uma arquitetura reconfigurável para uso na tomografia de Raios-X para amostras da física de solos. Ph.D. thesis, Universidade de São Paulo (2013)
Laia, M., Schiavoni, F., Madeira, D., Carvalho, D., Moreira, J.P., Paulo, A., Ferreira, R.: Copista – OMR system for historical musical collection recovery. In: Proceedings of the 12th International Symposium on Computer Music Multidisciplinary Research, p. 51, Marseille Cedex 13 – France: The Laboratory of Mechanics and Acoustics (2016)
Medina, R.A., Smith, L.A., Wagner, D.R.: Content-based indexing of musical scores. In: Proceedings of the 3rd ACM/IEEE-CS Joint Conference on Digital Libraries, JCDL 2003, pp. 18–26. IEEE Computer Society, Washington, DC, USA (2003)
Mundy, J.L., Zisserman, A., et al.: Geometric Invariance in Computer Vision, vol. 92. MIT Press, Cambridge (1992)
Nienhuys, H.-W., Nieuwenhuizen, J.: Lilypond, a system for automated music engraving. In: Proceedings of the XIV Colloquium on Musical Informatics (XIV CIM 2003), vol. 1. Citeseer (2003)
Oppenheim, I., Walshaw, C., Atchley, J.: The ABC standard 2.0 (2010)
Rebelo, A., Fujinaga, I., Paszkiewicz, F., Marcal, A., Guedes, C., Cardoso, J.: Optical music recognition: state-of-the-art and open issues. Int. J. Multimed. Inf. Retr. 1(3), 173–190 (2012)
Ripley, B.D.: Pattern Recognition and Neural Networks. Cambridge University Press, Cambridge (1996)
Selfridge-Field, E.: Beyond codes: issues in musical representation. In: Beyond MIDI, pp. 565–572. MIT Press (1997)
Szwoch, M.: A musical score recognition system. In: ICDAR, pp. 809–813 (2007)
Travis Pope, S.: Object-oriented music representation. Org. Sound 1(01), 56–68 (1996)
Xu, L., Krzyzak, A., Suen, C.: Methods of combining multiple classifiers and their applications to handwriting recognition. IEEE Trans. Syst. Man Cybern. 22(3), 418–435 (1992)
e Thiago Margarida, A.G.S.: Reconhecimento automático de símbolos em partituras musicais
Erpen, L.R.C.: Reconhecimento de padrões em imagens por descritores de forma. Universidade Federal do Rio Grande do Sul, Brazil (2004)
Souza, K.P., Pistori, H.: Implementção de um Extrator de Caracterısticas baseado em Momentos da Imagem
Hu, M.-K.: Visual pattern recognition by moment invariants. IRE Trans. Inf. Theory 8(2), 179–187 (1962)
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Laia, M. et al. (2017). The Framework of Copista: An OMR System for Historical Music Collection Recovery. In: Aramaki, M., Kronland-Martinet, R., Ystad, S. (eds) Bridging People and Sound. CMMR 2016. Lecture Notes in Computer Science(), vol 10525. Springer, Cham. https://doi.org/10.1007/978-3-319-67738-5_4
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