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An MEI-based standard encoding for hierarchical music analyses

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Abstract

We propose a standard representation for hierarchical musical analyses as an extension to the Music Encoding Initiative (MEI) representation for music. Analyses of music need to be represented in digital form for the same reasons as music: preservation, sharing of data, data linking, and digital processing. Systems exist for representing sequential information, but many music analyses are hierarchical, whether represented explicitly in trees or graphs or not. Features of MEI allow the representation of an analysis to be directly associated with the elements of the music analyzed. MEI’s basis in TEI (Text Encoding Initiative), allows us to design a scheme which reuses some of the elements of TEI for the representation of trees and graphs. In order to capture both the information specific to a type of music analysis and the underlying form of an analysis as a tree or graph, we propose related “semantic” encodings, which capture the detailed information, and generic “non-semantic” encodings which expose the tree or graph structure. We illustrate this with examples of representations of a range of different kinds of analysis.

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Notes

  1. See for example the Variations projects at the Indiana University, http://www.dlib.indiana.edu/projects/variations3/.

  2. http://musicontology.com/.

  3. https://voyant-tools.org.

  4. http://isophonics.net/content/reference-annotations-beatles.

  5. http://ddmal.music.mcgill.ca/research/billboard.

  6. https://github.com/craigsapp/bach-370-chorales.

  7. http://www.humdrum.org/ and https://csml.som.ohio-state.edu/Humdrum/.

  8. The relationships which a paradigmatic analysis shows, however, could be represented in the form of a graph, and so representation of paradigmatic analyses might be possible within the general framework we propose here. We leave development of this possibility to future work.

  9. Yust argues that a graph better represents elaborations which depend on the melodic interval from one note to another; for the same reasons a graph representation was proposed in [22] but subsequently abandoned as excessively complex in computational terms.

  10. http://gttm.jp/gttm/.

  11. A cadence is sequence of at least two chords helping to conclude a musical fragment.

  12. A reduction is the removal of what a given analysis technique considers as non-essential or less important notes (see Fig. 13).

  13. A progression is a sequence of chords or harmonic functions.

  14. http://relaxng.org/compact-tutorial.html.

  15. ‘One Document Does it all’, a literate programming language for XML schemas.

  16. A modulation is a temporary change of key.

References

  1. Bernabeu, J.F., Calera-Rubio, J., Iñesta, J.M., Rizo, D.: Melodic identification using probabilistic tree automata. J. New Music Res. 40(2), 93–103 (2011). https://doi.org/10.1080/09298215.2011.573560

    Article  Google Scholar 

  2. Bod, R.: A general parsing model for music and language. In: Music and Artificial Intelligence, pp. 77–90 (2002)

  3. Burgoyne, J.A., Wild, J., Fujinaga, I.: An expert ground truth set for audio chord recognition and music analysis. In: Proceedings of the 12th International Society for Music Information Retrieval Conference, Miami, Florida, USA, pp. 633–638 (2011)

  4. Fazekas, G., Raimond, Y., Jacobson, K., Sandler, M.: An overview of semantic web activities in the OMRAS2 project. J. New Music Res. 39(4), 295–311 (2010). https://doi.org/10.1080/09298215.2010.536555

    Article  Google Scholar 

  5. Gilbert, É., Conklin, D.: A probabilistic context-free grammar for melodic reduction. In: International Workshop on Artificial Intelligence and Music at IJCAI-07. Twentieth International Joint Conference on Artificial Intelligence, Hyderabad, India (2007)

  6. Gjerdingen, R.O.: A Classic Turn of Phrase: Music and the Psychology of Convention. University of Pennsylvania Press, Philadelphia (1988)

    Google Scholar 

  7. Good, M., Actor, G.: Using MusicXML for file interchange. In: International Conference on Web Delivering of Music, p. 153 (2003). https://doi.org/10.1109/WDM.2003.1233890

  8. Hamanaka, M., Keiji, H., Satoshi, T.: Implementing “a generative theory of tonal music”. J. New Music Res. 35(4), 249–277 (2006)

    Article  MATH  Google Scholar 

  9. Hamanaka, M., Hirata, K., Tojo, S.: Musical structural analysis database based on GTTM. In: Proceedings of the 15th International Society for Music Information Retrieval Conference, ISMIR 2014, Taipei, Taiwan, October 27–31, pp. 325–330 (2014)

  10. Hankinson, A., Roland, P., Fujinaga, I.: The music encoding initiative as a document-encoding framework. In: ISMIR (2011)

  11. Harte, C., Sandler, M., Abdallah, S., Gómez, E.: Symbolic representation of musical chords: a proposed syntax for text annotations, pp. 66–71. London (2005)

  12. Högberg, J.: Wind in the willows: generating music by means of tree transducers. In: CIAA, pp. 153–162 (2005)

  13. Humphrey, E.J., Salamon, J., Nieto, O., Forsyth, J., Bittner, R.M., Bello, J.P.: JAMS: a JSON annotated music specification for reproducible MIR research. In: Proceedings of the 15th International Society for Music Information Retrieval Conference, ISMIR 2014, Taipei, Taiwan, October 27–31, pp. 591–596 (2014)

  14. Ide, N.M., Sperberg-McQueen, C.M.: The TEI: history, goals, and future. Comput. Humanit. 29(1), 5–15 (1995)

    Article  Google Scholar 

  15. Jacquemard, F., Donat-Bouillud, P., Bresson, J.: A structural theory of rhythm notation based on tree representations and term rewriting. In: Advances in Artificial Intelligence, pp. 3–15. Springer, Cham (2015)

  16. Kirlin, P.B.: A data set for computational studies of Schenkerian analysis. In: Proceedings of the 15th International Society for Music Information Retrieval Conference, ISMIR 2014, Taipei, Taiwan, October 27–31, pp. 213–218 (2014a)

  17. Kirlin, P.B.: A probabilistic model of hierarchical music analysis. Ph.D. thesis, University of Massachusetts Amhers, MA (2014b)

  18. Lee, C.S.: The rhythmic interpretation of simple musical sequences: towards a perceptual model. In: West, R., Howell, P., Cross, I. (eds.) Musical Structure and Cognition, pp. 53–69. Academic Press, London (1985)

    Google Scholar 

  19. Lerdahl, F., Jackendoff, R.: A Generative Theory of Tonal Music. MIT Press, Cambridge (1983)

    Google Scholar 

  20. Linster, C.: On analyzing and representing musical rhythm. In: Balaban, M., Ebcioğlu, K., Laske, O. (eds.) Understanding Music with AI: Perspectives on Music Cognition, pp. 414–427. AAAI Press, Menlo Park (1992)

    Google Scholar 

  21. Lisena, P., Todorov, K., Cecconi, C., Leresche, F., Canno, I., Puyrenier, F., Voisin, M., Le Meur, T., Troncy, R.: Controlled vocabularies for music metadata. In: Proceedings of the 19th ISMIR Conference, Paris, France, September 23–27, pp. 424–430 (2018)

  22. Marsden, A.: Representing melodic patterns as networks of elaborations. Comput. Humanit. 35(1), 37–54 (2001)

    Article  Google Scholar 

  23. Marsden, A.: Generative structural representation of tonal music. J. New Music Res. 34(4), 409–428 (2005). https://doi.org/10.1080/09298210600578295

    Article  Google Scholar 

  24. Marsden, A.: Schenkerian analysis by computer: a proof of concept. J. New Music Res. 39(3), 269–289 (2010). https://doi.org/10.1080/09298215.2010.503898

    Article  Google Scholar 

  25. Nattiez, J.-J.: Fondements d’une sémiologie de la musique. Union Générale d’Éditions, Paris (1975)

    Google Scholar 

  26. Pinto, A., Tagliolato, P.: A generalized graph-spectral approach to melodic modeling and retrieval. In: MIR ’08, Proceeding of the 1st ACM International Conference on Multimedia Information Retrieval, pp. 89–96. ACM, New York (2008)

  27. Rashid, S., McGuinness, D.L., De Roure, D.: A music theory ontology. In: In International Workshop on Semantic Applications for Audio and Music-ISWC. Monterey (2018)

  28. Rizo, D.: Symbolic music comparison with tree data structures. Ph.D. thesis, Universidad de Alicante (2010)

  29. Rizo, D., Marsden, A.: A standard format proposal for hierarchical analyses and representations. In: Proceedings of the 3rd International workshop on Digital Libraries for Musicology, pp. 25–32. ACM Press, New York (2016)

  30. Sapp, C.S.: Computational methods for the analysis of musical structure. Ph.D. thesis, Stanford University (2011)

  31. Schenker, H.: Free Composition (Der Freie Satz). Longman, London (1979) (1935)

  32. Selfridge-Field, E.: Beyond MIDI: The Handbook of Musical Codes. MIT Press, Cambridge (1997)

    Google Scholar 

  33. Uboldi, G., Caviglia, G.: Information visualizations and interfaces in the humanities. In: David, B. (ed.) New Challenges for Data Design, pp. 207–218. Springer, London (2014)

    Google Scholar 

  34. Weigl, D.M., Lewis, D., Crawford, T., Knopke, I., Page, K.R.: On providing semantic alignment and unified access to music library metadata. Int. J. Digit. Libr. (2017). https://doi.org/10.1007/s00799-017-0223-9

  35. Yust, J.: The geometry of melodic, harmonic, and metrical hierarchy. In: Mathematics and Computation in Music, pp. 180–192. Springer, Berlin (2009)

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Acknowledgements

We would like to thank Eleanor Selfridge-Field, Don Byrd, Tom Collins, and Phillip Kirlin for contributions during the MEC 2016 and DLfM 2016 conferences which have informed this work, and to thank Ichiro Fujinaga and Perry Roland for their encouragement to continue it.

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Authors and Affiliations

  1. Department of Software and Computing Systems, University of Alicante, Alicante, Spain

    David Rizo

  2. Instituto Superior de Enseñanzas Artísticas de la Comunidad Valenciana, Valencia, Spain

    David Rizo

  3. Lancaster Institute for the Contemporary Arts, Lancaster University, Lancaster, UK

    Alan Marsden

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Correspondence to David Rizo.

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This work was partially supported by the Spanish Ministerio de Economía, Industria y Competitividad through HispaMus Project (TIN2017-86576-R).

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Rizo, D., Marsden, A. An MEI-based standard encoding for hierarchical music analyses. Int J Digit Libr 20, 93–105 (2019). https://doi.org/10.1007/s00799-018-0262-x

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