Staff Line Removal Algorithm and Research Based on Run-Length Graph Slice and Topological Structure of Music

Abstract:

Staff line removal is a key step before segmentation and recognition of music image and plays an important role in OMR (Optical Music Recognition) research, the result of staff line removal directly influences the performance and function of the whole OMR system. However, over-removal and under-removal often occurs in the processing and leads to the low efficiency of music recognition rate. So, in order to solve the arduous problem, an approach based on run-length graph slice and topological structure of music is put forward by careful analysis of staff line and music notation structure. Experience results show the validity and practicality of the presented algorithm fast and effectively.

• Periodical: Advanced Materials Research (Volumes 760-762)
• Pages: 1429-1433
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.760-762.1429
• Online since: September 2013
• Authors: Yin Xian Yang, Li Zhao, Cai Rong Zou, Yin Xian Yang
• Keywords: OMR, Run-Length Graph Slice, Staff Line Removal, Topological Structure

References

[1] Ng, K.C. Boyle, R.D. Recognition and reconstruction of primitives in music scores [J] . Image and Vision Computing. 1996: 14(1): 39-46.

DOI: 10.1016/0262-8856(95)01038-6

[2] Rebelo, A. Capela, G, Jaime S . Optical recognition of music symbols, International Journal on Document Analysis and Recognition [J]. 2010: 13(1): 19-31.

DOI: 10.1007/s10032-009-0100-1

[3] Vieira, P. Rovisco, Pais, Pinto, J.C. Recognition of musical symbols in ancient manuscripts . IEEE International Conference on Image Processing [C]. 2001(3) 38-41.

DOI: 10.1109/icip.2001.958045

[4] Kobayakawa, Tatsu. Auto music score recognizing system. Proceedings of SPIE - The International Society for Optical Engineering [C]. (1993).

[5] Fujinaga, Ichiro. Optical music recognition system which learns . Proceedings of SPIE - The International Society for Optical Engineering [C]. 1993 210-217.

[6] Rossant, Florence. Bloch, Isabelle. A fuzzy model for optical recognition of musical scores. Fuzzy Sets and Systems [J]. 2004: 141(2): 165-201.

DOI: 10.1016/s0165-0114(03)00094-0

[7] Szwoch, Mariusz. Guido: A musical score recognition system. Proceedings of the International Conference on Document Analysis and Recognition [C]. 2007(2) 809-813.

DOI: 10.1109/icdar.2007.4377027

[8] Pugin L, Burgoyne JA, Fujinaga, Goal-Directed Evaluation for the Improvement of Optical Music Recognition on Early Music Prints . 17th ACM/IEEE Joint Conference on Digital Libraries, 2007, 6.

DOI: 10.1145/1255175.1255233

[9] Homenda W, Optical music recognition: The case study of pattern recognition. 4th International Conference on Computer Recognition Systems (CORES 05). 2005, 3.

[10] Chen Genfang; Xia Shunren, The study and prototype system of printed music recognition. International Conference on Neural Networks and Signal Processing [C]. 2003, 10.

DOI: 10.1109/icnnsp.2003.1281038

[11] Bainbridge D, Bell T. A music notation construction engine for optical music recognition [J]. Software Practice & Experience. 2003: 33(2): 173-200.

DOI: 10.1002/spe.502

[12] Bainbridge D, Bell T, The challenge of optical music recognition [J]. Computers and the Humanities. 2001: 35(2): 95-121.

[13] Fujinaga, Ichiro, Adaptive optical music recognition，Ph.D., McGill University (Canada), (1997).

[14] Ng, K.C., Automated computer recognition of music score Ph.D. University of Leeds (United Kingdom), (1995).

[15] Vieira, P.; Pinto, J.C. Recognition of musical symbols in ancient manuscripts. International Conference on Image Processing, 2001. Proceedings [C]. 2001 38 – 41.

DOI: 10.1109/icip.2001.958045

[16] Cardoso, Jaime S. Capela, Artur. Rebelo, Ana. A connected path approach for staff detection.

[17] Cardoso, J. Capela, A.; Rebelo, A. Staff Detection with Stable Paths [J], IEEE Transactions on Pattern Analysis and Machine Intelligence. 2009: 31(6): 1134 – 1139.

DOI: 10.1109/tpami.2009.34

[18] Dalitz, C.; Droettboom, M. Pranzas, B. Fujinaga, I. A Comparative Study of Staff Removal Algorithms. IEEE Transactions on Pattern Analysis and Machine Intelligence [J]. 2008: 30(5): 753 – 766.

DOI: 10.1109/tpami.2007.70749