Towards a Standard Testbed for Optical Music Recognition: Definitions, Metrics, and Page Images

References

• Bainbridge, D., & Bell, T. (2001). The challenge of optical music recognition. Computers and the Humanities, 35(2), 95–121.
   Google Scholar
• Bellini, P., Bruno, I., & Nesi, P. (2007). Assessing Optical Music Recognition Tools. Computer Music Journal, 31(1), 68–93.
   Web of Science ®Google Scholar
• Bellini, P., Bruno, I., & Nesi, P. (2003, rev. 2014). Assessment of optical music recognition tools, OMR assessment (type 1) Working Paper. http://www.disit.org/5932
   Google Scholar
• Bugge, E.P., Juncher, K.L., Mathiasen, B.S., & Simonsen, J.G. (2011). Using sequence alignment and voting to improve optical music recognition from multiple recognizers. Proceedings of the 12th International Society for Music Information Retrieval Conference (ISMIR 2011) (pp. 405–410). Canada: International Society for Music Information Retrieval.
   Google Scholar
• Byrd, D. (1984). (Doctoral dissertation, Computer Science Dept., Indiana University). Ann Arbor, Michigan: UMI ProQuest (order no. 8506091); also available from. Retrieved (in scanned form). Music notation by computer. http://www.informatics.indiana.edu/donbyrd/Papers/DonDissScanned.pdf
   Google Scholar
• Byrd, D. (1994). Music-notation software and intelligence. Computer Music Journal, 18(1), 17–20. Available (in scanned form) at http://www.informatics.indiana.edu/donbyrd/Papers/MusNotSoftware+Intelligence.pdf
   Web of Science ®Google Scholar
• Byrd, D. (2008). Guidelines for choosing OMR test pages (and factors affecting OMR accuracy). MeTAMuSE/IU Project Working Paper. Retrieved from http://www.informatics.indiana.edu/donbyrd/MROMR2010Pap/
   Google Scholar
• Byrd, D. (2013). Gallery of interesting music notation. Retrieved from http://www.informatics.indiana.edu/donbyrd/InterestingMusicNotation.html
   Google Scholar
• Byrd, D., & Schindele, M. (2006). Prospects for improving OMR with multiple recognizers. Proceedings of the 7th International Conference on Music Information Retrieval (ISMIR 2006) (pp. 41–46). Canada: University of Victoria.
   Google Scholar
• Byrd, D., & Schindele, M. (2007). Prospects for improving OMR with multiple recognizers, revised and expanded version. Retrieved from http://www.informatics.indiana.edu/donbyrd/MROMRPap
   Google Scholar
• Byrd, D., Guerin, W., Schindele, M., & Knopke, I. (2010). OMR evaluation and prospects for improved OMR via multiple recognizers. Retrieved from http://www.informatics.indiana.edu/donbyrd/MROMR2010Pap/OMREvaluation+Prospects4MROMR.doc
   Google Scholar
• Cleverdon, C. (1967). The Cranfield tests on index language devices. In K. Sparck Jones & P. Willett (Eds.), (1997) Readings in information retrieval. San Francisco: Morgan Kaufmann.
   Google Scholar
• Droettboom, M., & Fujinaga, I. (2004). Micro-level groundtruthing environment for OMR. In Proceedings of the 5th International Conference on Music Information Retrieval (ISMIR 2004). Barcelona: Universitat Pompeu Fabra. (pp. 497–500)
   Google Scholar
• Gomberg, D. (1975). (Doctoral dissertation, Computer Science Dept., Washington University) A Computer-Oriented System for Music Printing, Ann Arbor, Michigan: UMI ProQuest.
   Google Scholar
• Gould, E. (2011). Behind bars. London: Faber Music.
   Google Scholar
• Hofstadter, D. (1979; twentieth-anniversary edition, 1999). Gödel, Escher, Bach: An eternal golden braid. New York: Basic Books.
   Google Scholar
• Hook, J. (2011). How to perform impossible rhythms. Music Theory Online, 17(4), article 1.
   Google Scholar
• Jones, G., Ong, B., Bruno, I., & Ng, K. (2008). Optical music imaging: music document digitisation, recognition, evaluation, and restoration. Interactive multimedia music technologies (pp. 50–79). Hershey: IGI Global.
   Google Scholar
• Kanai, J., Nartker, T.A., Rice, S.V., & Nagy, G. (1993). Performance metrics for document understanding systems. Proceedings of the 2nd International Conference on Document Analysis and Recognition (pp. 424–427). Piscataway, NJ: IEEE.
   Google Scholar
• Knopke, I., & Byrd, D. (2007). Towards MusicDiff: A foundation for improved optical music recognition using multiple recognizers. In Proceedings of the 8th International Conference on Music Information Retrieval (ISMIR 2007) (pp. 123–126). Vienna: Austrian Computer Society (OCG).
   Google Scholar
• McCarthy, J., & Hayes, P. J. (1969). Some philosophical problems from the standpoint of artificial intelligence. Machine Intelligence, 4, 463–502.
   Google Scholar
• McPherson, J. (2002). Introducing feedback into an optical music recognition system. Proceedings of the 3rd International Conference on Music Information Retrieval (ISMIR 2002) (pp. 259–260). Paris: IRCAM Centre Pompidou.
   Google Scholar
• Nagy, G. (1995). Document image analysis: Automated performance evaluation. In A. K. Spitz & A. Dengel (Eds.), Document analysis systems (pp. 137–156). Singapore: World Scientific.
   Google Scholar
• Ng, K.C., & Jones, A. (2003). A quick-test for optical music recognition systems. Paper presented at the 2nd MUSICNETWORK Open Workshop, Workshop on Optical Music Recognition System, Leeds, September 2003.
   Google Scholar
• Raphael, C., & Wang, J. (2011). New approaches to optical music recognition. Proceedings of the 12th International Society for Music Information Retrieval Conference (ISMIR 2011) (pp. 305–310). Canada: International Society for Music Information Retrieval.
   Google Scholar
• Rebelo, A., Fujinaga, I., Paszkiewicz, F., Marcal, A.R.S., Guedes, C., & Cardoso, J.S. (2012). Optical music recognition: state-of-the-art and open issues. International Journal of Multimedia Information Retrieval, 1(3), 173–190.
   Google Scholar
• Reed, K.T. (1995). Optical music recognition, (MSc thesis). Dept. of Computer Science, University of Calgary, Canada.
   Google Scholar
• Riley, J., & Fujinaga, I. (2003). Recommended best practices for digital image capture of musical scores. OCLC Systems & Services, 19(2), 62–69.
   Google Scholar
• Ross, T. (1970). The art of music engraving and processing. Miami: Hansen.
   Google Scholar
• Rossant, F., & Bloch, I. (2007). Robust and adaptive OMR system including fuzzy modeling, fusion of musical rules, and possible error detection. EURASIP Journal on Advances in Signal Processing, 2007, article ID 81541.
   Google Scholar
• Russell, D. (1997). Popular music in England 1840–1914: A social history. Manchester University Press.
   Google Scholar
• Sapp, C. (2008). SharpEye examples: Mozart Piano Sonata No. 13 in B major, K 333 and Farewell to Lochaber. Retrieved from http://craig.sapp.org/omr/sharpeye
   Google Scholar
• Sapp, C. (2013). OMR comparison of SmartScore and SharpEye. Retrieved from https://ccrma.stanford.edu/~craig/mro-compare-beethoven
   Google Scholar
• Selfridge-Field, E., Carter, N., & McGee, W. (1994). Optical recognition: A survey of current work; An interactive system; Recognition problems; The issue of practicality. In W. Hewlett & E. Selfridge-Field (Eds.), Computing in Musicology, Vol. 9 (pp. 107–166). Cambridge, MA: MIT Press.
   Google Scholar
• Szwoch, M. (2008). Using MusicXML to evaluate accuracy of OMR systems. Proceedings of the 5th international Conference on Diagrammatic Representation and Inference ( LNCS 5223, pp. 419–422). Berlin: Springer-Verlag.
   Google Scholar
• Zanibbi, R., & Blostein, D. (2011). Recognition and retrieval of mathematical expressions. International Journal on Document Analysis and Recognition, 15(4), 331–357.
   Web of Science ®Google Scholar
• Zanibbi, R., Pillay, A., Mouchère, H., Viard-Gaudin, C., & Blostein, D. (2011). Stroke-based performance metrics for handwritten mathematical expressions. Proceedings of the International Conference on Document Analysis and Recognition, ICDAR 2011 (pp. 334–338). Piscataway, NJ: IEEE.
   Google Scholar