A Method for Music Symbols Extraction based on Musical Rules

Optical Music Recognition (OMR) systems are an important tool for the automatic recognition of digitized music scores. However, handwritten musical scores are especially problematic for an automatic recognition. They have irregularities that go from heterogeneous illumination to variability in symbols shape and complexity inherent to music structure. These issues cause serious difficulties when one wants a robust OMR system facilitating search, retrieval and analysis operations. To transform the paper-based music scores and manuscripts into a machine-readable symbolic format several consistent algorithms are needed. In this paper a method for music symbols extraction in handwritten and printed scores is presented. This technique tries to incorporate musical rules as prior knowledge in the segmentation process in order to overcome the state of the art results.

[1]  Sylvie Philipp-Foliguet,et al.  Printed music recognition , 1993, Proceedings of 2nd International Conference on Document Analysis and Recognition (ICDAR '93).

[2]  David Cooper,et al.  Embracing the Composer: Optical Recognition of Handwritten Manuscripts , 1999, International Conference on Mathematics and Computing.

[3]  Isabelle Bloch,et al.  Robust and Adaptive OMR System Including Fuzzy Modeling, Fusion of Musical Rules, and Possible Error Detection , 2007, EURASIP J. Adv. Signal Process..

[4]  Gilson A. Giraldi,et al.  Music Score Binarization Based on Domain Knowledge , 2011, IbPRIA.

[5]  J. W. Roach,et al.  Using domain knowledge in low-level visual processing to interpret handwritten music: An experiment , 1988, Pattern Recognit..

[6]  Jim R. Parker,et al.  Automatic computer recognition of printed music , 1996, Proceedings of 13th International Conference on Pattern Recognition.

[7]  Ichiro Fujinaga,et al.  A Comparative Study of Staff Removal Algorithms , 2008, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[8]  Pierfrancesco Bellini,et al.  Assessing Optical Music Recognition Tools , 2007, Computer Music Journal.

[9]  Alicia Fornés,et al.  Primitive Segmentation in Old Handwritten Music Scores , 2005, GREC.

[10]  Kenji Shoji,et al.  Symbol Recognition of Printed Piano Scores with Touching Symbols , 2006, 18th International Conference on Pattern Recognition (ICPR'06).

[11]  Nicholas Paul Carter,et al.  Automatic recognition of printed music in the context of electronic publishing , 1989 .

[12]  Jaime S. Cardoso,et al.  Optical recognition of music symbols , 2010, International Journal on Document Analysis and Recognition (IJDAR).