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In the paper optical music recognition (OMR) is considered as an example of paper-to-compute-memory data flow. This specific area of interest forces specific methods to be applied in data processing, but in principle, gives a perspective on the merit of the subject of data aggregation. The process of paper-to-computer-memory music data flow is presented from the perspective of the process of acquiring information from plain low-level data. The discussion outlines an interpretation of this process as a metaphor of granular computing. The stages of data aggregation and data abstraction are shown as steps leading to the formation of knowledge granules and to recovering dependencies between knowledge granules and between the information included in knowledge granules. An influence of the granular world of music notation on the design of a computer program is presented. the presentation is related to a real computer program of music notation recognition and music knowledge representation and processing. The relationship between the granular structure of music knowledge and user interface of the program is outlined.
Czasopismo
Rocznik
Tom
Strony
433--455
Opis fizyczny
Bibliogr. 24 poz., rys., tab.
Twórcy
autor
- Faculty of Mathematics and Information Science, Warsaw University of Technology
Bibliografia
- [1] J.P. Armand: Musical score recognition: A hierarchical and recursive approach. Proc. 2nd Int. Conf. on Document Analysis and Recognition, (Cat. No.93TH0578-5), (1993), 906-909.
- [2] A. Bargiela and W. Pedrycz: Classification and Clustering of Granular Data. Joint 9th IFSA World Congress and 20th NAFIPS Int. Conf., Vancouver, (2001), 1696-1701.
- [3] K. Barbar, M. Desainte-Catherine and A. Miniussi: The semantics of Musical Hierarchies. Computer Music J., 17(3), (1993), 30-37.
- [4] D. Blostein and H.S.Baird: A Critical Survey of Music Image Analysis. In H.S.Baird, H.Bunke, K.Yamamoto (Eds), Structured Document Analysis, Springer-Verlag, (1992), 405-434.
- [5] N. Carter and R. Bacon: Automatic Recognition of Printed Music. In H.S. Baird, H.Bunke, K. Yamamoto (Eds), Structured Document Analysis, Springer-Verlag, (1992).
- [6] A. Cooper: About Face: The essentials of User Interface Design. IDG Books Worldwide, Inc., 1995.
- [7] H. Fahmy and D. Blostein: A graph grammar programming style for recognition of music notation. Machine Vision and Applications, 6 (1993), 83-99.
- [8] M. Ferrand and A. Cardoso: Scheduling to reduce uncertainty in syntactical music structures. Advances in Artificial Intelligence. Proc. 14th Brazilian Symp. on Artificial Intelligence, (1998), 249-258.
- [9] I. Fujinaga: Optical music recognition using projections. Master’s thesis, McGill University, Montreal, Canada, 1988.
- [10] T.W. Goolsby: Eye movement in music reading: Effects of reading ability, notational complexity, and encounters. Music Perception, 12(1), (1994), 77-96.
- [11] T.W. Goolsby: Profiles of processing: Eye movements during sightreading. Music Perception, 12(1), (1994), 97-123.
- [12] W. Homenda: Optical pattern recognition for printed music notation. Proc. SPIE’s Int. Symp. on Aerospace/Defense Sensing & Control and Dual-Use Photonics, Orlando, USA, (1995), 2490: 230-239.
- [13] W.Homenda: Optical Music Recognition: the Case of Granular Computing. In Granular Computing: An Emerging Paradigm, Physica Verlag/Springer Verlag, (2001), 341-360.
- [14] T.Kinoshita, et. al.: Note recognition using statistical information of musical note transitions. J. Acoustical Society of Japan, 54(3), (1998), 190-198.
- [15] G.E. Kopec, et.al.: Markov source model for printed music decoding. Proc. SPIE - The International Society for Optical Engineering, (1995), 2422: 115-125.
- [16] B.R. Modayur, et.al.: MUSER: a prototype musical recognition system using mathematical morphology. Machine Vision and Applications, 6(2-3), (1993), 140-150.
- [17] NIFF, Notation Interchange File Format, Document ver. 6a.3, October 1998.
- [18] W. Pedrycz: Neural Networks in the Framework of Granular Computing. Manuscript, 2001.
- [19] W. Pedrycz: Granular Computing: An Introduction. Joint 9th IFSA World Congress and 20th NAFIPS Int. Conf., Vancouver, (2001), 1349-1354.
- [20] D.S. Prerau: Do-re-mi: A program that recognizes music notation. Computers and Humanities, 9 (1975), 25-29.
- [21] T. Ross: The Art of Music Engraving and Processing. Hansen Books, Miami, 1970.
- [22] Smart Score v.2.0, www.musitek.com, Ojai, CA, October, 2001.
- [23] K. Stone: Music Notation in Twentieth Century: A Practical Guidebook. W.W. Norton & Co.,New York, 1980.
- [24] H. Taube: Stella: Persistent Score Representation and Score Editing in Common Music. Computer Music J., 17(3), (1993), 38-50.
- [25] WWWPage, Optical Music Recognition Bibliography, by Ichiro Fujinaga, October 2000: http://gigue.peabody.jhu.edu/ich/research/omr/omrbib.html
Typ dokumentu
Bibliografia
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bwmeta1.element.baztech-article-BSW3-0003-0007