ABSTRACT
Comparing music score files is an important task for many activities such as collaborative score editing, version control and evaluation of optical music recognition (OMR) or music transcription. Following the Unix diff model for text files, we propose an original procedure for computing the differences between two score files, typically in XML format. It performs a comparison of scores at the notation (graphical) level, based on a new intermediate tree representation of the music notation content of a score and a combination of sequence- and tree-edit distances. We also propose a tool to visualize the differences between two scores side-by-side, using the music notation engraving library Verovio, and we employ it to test the procedure on an OMR dataset.
- Carlos Agon, Karim Haddad, and Gérard Assayag. 2002. Representation and rendering of rhythm structures. In Second International Conference on Web Delivering of Music (WEDELMUSIC). IEEE, 109–113.Google ScholarCross Ref
- Julien Allali, Pascal Ferraro, Pierre Hanna, Costas Iliopoulos, and Matthias Robine. 2009. Toward a general framework for polyphonic comparison. Fundamenta Informaticae 97, 3 (2009), 331–346.Google ScholarDigital Library
- Christopher Antila, Jeffrey Treviño, and Gabriel Weaver. 2017. A hierarchic diff algorithm for collaborative music document editing. In Third International Conference on Technologies for Music Notation and Representation (TENOR).Google Scholar
- José F Bernabeu, Jorge Calera-Rubio, José M Iñesta, and David Rizo. 2011. Melodic identification using probabilistic tree automata. Journal of New Music Research 40, 2 (2011), 93–103.Google ScholarCross Ref
- Philip Bille. 2005. A survey on tree edit distance and related problems. Theor. Comput. Sci. 337, 1-3 (2005), 217–239. https://doi.org/10.1016/j.tcs.2004.12.030Google ScholarDigital Library
- Jorge Calvo-Zaragoza and David Rizo. 2018. End-to-End Neural Optical Music Recognition of Monophonic Scores. Applied Sciences 8 (04 2018), 606. https://doi.org/10.3390/app8040606Google Scholar
- Gregory Cobena, Serge Abiteboul, and Amelie Marian. 2002. Detecting Changes in XML Documents. In Proceedings 18th International Conference on Data Engineering. IEEE, 41–52.Google ScholarCross Ref
- Andrea Cogliati and Zhiyao Duan. 2017. A Metric for Music Notation Transcription Accuracy. In Proceedings of the 18th International Society for Music Information Retrieval Conference (ISMIR). 407–413.Google Scholar
- Michael Scott Cuthbert and Christopher Ariza. 2010. music21: A toolkit for computer-aided musicology and symbolic music data. In ISMIR. International Society for Music Information Retrieval.Google Scholar
- Francesco Foscarin, David Fiala, Florent Jacquemard, Philippe Rigaux, and Virginie Thion. 2018. Gioqoso, an online Quality Assessment Tool for Music Notation. In 4th International Conference on Technologies for Music Notation and Representation (TENOR’18).Google Scholar
- Francesco Foscarin, Florent Jacquemard, Philippe Rigaux, and Masahiko Sakai. 2019. A Parse-based Framework for Coupled Rhythm Quantization and Score Structuring. In International Conference on Mathematics and Computation in Music. Springer, 248–260.Google ScholarDigital Library
- Elaine Gould. 2011. Behind Bars: The Definitive Guide to Music Notation. Faber Music.Google Scholar
- Paul Heckel. 1978. A technique for isolating differences between files. Commun. ACM 21, 4 (1978), 264–268.Google ScholarDigital Library
- James Wayne Hunt and M Douglas MacIlroy. 1976. An algorithm for differential file comparison. Technical Report. Bell Laboratories Murray Hill.Google Scholar
- James W Hunt and M Douglas McIlroy. 1976. An algorithm for differential le comparison. Computing Science Technical Report 41 (1976).Google Scholar
- David Huron. 1997. Humdrum and Kern: Selective feature encoding. In Beyond MIDI. MIT Press, 375–401.Google Scholar
- Ian Knopke and Donald Byrd. 2007. Towards musicdiff: A foundation for improved optical music recognition using multiple recognizers. Dynamics 85, 165 (2007), 121.Google Scholar
- Kjell Lemström. 2000. String Matching Techniques for Music Retrieval. Ph.D. Dissertation. University of Helsinki, Department of Computer Science.Google Scholar
- Andrew Mcleod and Mark Steedman. 2018. Evaluating Automatic Polyphonic Music Transcription. In Proceedings of the 19th International Society for Music Information Retrieval Conference, ISMIR 2018, Paris, France, September 23-27, 2018. 42–49.Google Scholar
- Marcel Mongeau and David Sankoff. 1990. Comparison of musical sequences. Computers and the Humanities 24, 3 (1990), 161–175.Google ScholarCross Ref
- Eugene W Myers. 1986. AnO (ND) difference algorithm and its variations. Algorithmica 1, 1-4 (1986), 251–266.Google ScholarDigital Library
- Laurent Pugin, Rodolfo Zitellini, and Perry Roland. 2014. Verovio: A library for Engraving MEI Music Notation into SVG. In ISMIR. 107–112.Google Scholar
- David Rizo. 2010. Symbolic music comparison with tree data structures. Universidad de Alicante.Google Scholar
- Perry Roland. 2002. The music encoding initiative (mei). In Proceedings of the First International Conference on Musical Applications Using XML, Vol. 1060.Google Scholar
- Esko Ukkonen. 1985. Algorithms for approximate string matching. Information and control 64, 1-3 (1985), 100–118.Google Scholar
- Robert A Wagner and Michael J Fischer. 1974. The string-to-string correction problem. Journal of the ACM (JACM) 21, 1 (1974), 168–173.Google ScholarDigital Library
- Kaizhong Zhang and Dennis Shasha. 1989. Simple fast algorithms for the editing distance between trees and related problems. SIAM journal on computing 18, 6 (1989), 1245–1262.Google Scholar
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