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Encoding music performance data in Humdrum and MEI

International Journal on Digital Libraries volume 20, pages 81–91 (2019)Cite this article

Abstract

This paper proposes extensions to two existing music encoding formats, Humdrum and Music Encoding Initiative (MEI), in order to facilitate linking music performance data with corresponding score information. We began by surveying music scholars about their needs for encoding timing, loudness, pitch, and timbral performance data. We used the results of this survey to design and implement new spines in Humdrum syntax to encode summary descriptors at note, beat, and measure levels and new attributes in the MEI format to encode both note-wise summaries and continuous data. These extensions allow for multiple performances of the same piece to be directly compared with one another, facilitating both humanistic and computational study of recorded musical performances.

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Notes

  1. http://mtg.upf.edu/download/datasets/eep-dataset.

  2. https://code.soundsoftware.ac.uk/projects/dai2015analysis-resources.

  3. http://www.mazurka.org.uk/.

  4. http://www.sonicvisualiser.org/.

  5. http://www.vamp-plugins.org/.

  6. http://www.ampact.org/.

  7. http://www.tei-c.org/Vault/P4/.

  8. http://www.tei-c.org/Guidelines/P5/.

  9. The MEI.performanceEXT module is available as an ODD at: https://github.com/jcdevaney/meiPerformanceExt.

  10. http://web.mit.edu/music21/.

  11. http://www.ampact.org.

References

  1. Berry, W.: Musical Structure and Performance. Yale University Press, New Haven (1989)

    Book  Google Scholar 

  2. Cannam, C., Landone, C., Sandler, M., Bello, J.P.: The sonic visualiser: a visualisation platform for semantic descriptors from musical signals. In: Proceedings of the International Conference on Music Information Retrieval (ISMIR), pp. 324–327 (2006)

  3. Cone, E.T.: Musical Form and Musical Performance. W.W. Norton, New York (1968)

    Google Scholar 

  4. Cook, N.: The conductor and the theorist: Furtwängler, Schenker and the first movement of beethoven’s ninth symphony. In: Rink, J. (ed.) The Practice of Performance: Studies in Musical Interpretation, pp. 105–125. Cambridge University Press, Cambridge (1995)

    Chapter  Google Scholar 

  5. Cook, N.: Analysing performance and performing analysis. In: Cook, N., Everist, M. (Eds.) Rethinking Music, pp. 239–261. Oxford University Press, Oxford (1999)

  6. Cook, N.: Between process and product: music and/as performance. Music Theory Online 7(2) (2001)

  7. Cook, N.: Performance analysis and Chopin’s mazurkas. Music. Sci. 11(2), 183–207 (2007)

    Article  Google Scholar 

  8. Cook, N.: Beyond the Score: Music as Performance. Oxford University Press, Oxford (2014)

    Book  Google Scholar 

  9. Dai, J., Mauch, M., Dixon, S.: Analysis of intonation trajectories in solo singing. In: Proceedings of the International Society for Music Information Retrieval Conference (ISMIR), pp. 42–46 (2015)

  10. Devaney, J., Léveillé Gauvin, H.: Representing and linking music performance data with score information. In: Proceedings of the 3rd International Workshop on Digital Libraries for Musicology, pp. 1–8. ACM (2016)

  11. Devaney, J., Mandel, M., Fujinaga, I.: A study of intonation in three-part singing using the automatic music performance analysis and comparison toolkit (AMPACT). In: Proceedings of the International Society for Music Information Retrieval Conference (ISMIR), pp. 511–516 (2012)

  12. Dixon, S.: Automatic extraction of tempo and beat from expressive performances. J. New Music Res. 30(1), 39–58 (2001)

    MathSciNet  Article  Google Scholar 

  13. Gingras, B., McAdams, S., Schubert, P.: The performer as analyst: a case study of J. S. Bach’s ‘Dorian’ Fugue (BWV 538). In: Music Theory and Interdisciplinarity—8th Congress of the Gesellschaft für Musiktheorie Graz 2008, pp. 305–318 (2010)

  14. Goodchild, M., Gingras, B., McAdams, S.: Analysis, performance, and tension perception of an unmeasured prelude for harpsichord. Music Percept. Interdiscip. J. 34(1), 1–20 (2016)

    Article  Google Scholar 

  15. Hirata, K., Noike, K., Haruhiro, K.: A proposal for a performance data format. In: Proceedings of the Workshop on Methods for Automatic Music Performance and Their Applications in a Public Rendering Contest (IJCAI-03), pp. 65–69 (2003)

  16. Howell, T.: Analysis and performance: the search for a middleground. In: Paynter, J. (Ed.) Companion to Contemporary Musical Thought, vol. 2, pp. 692–714. Routledge, London (1992)

  17. Huron, D.: The Humdrum Toolkit: Reference Manual. Center for Computer Assisted Research in the Humanities, Menlo Park (1995)

    Google Scholar 

  18. Ide, N., Véronis, J.: Text Encoding Initiative: Background and Contexts, vol. 29. Springer, Berlin (1995)

    Book  Google Scholar 

  19. Lester, J.: Performance and analysis: interaction and interpretation. In: Rink, J. (Ed.) The Practice of Performance, pp. 197–216. Cambridge University Press, Cambridge, UK (1995)

  20. Marchini, M., Ramirez, R., Papiotis, P., Maestre, E.: The sense of ensemble: a machine learning approach to expressive performance modelling in string quartets. J. New Music Res. 43(3), 303–317 (2014)

    Article  Google Scholar 

  21. Mauch, M., Cannam, C., Bittner, R., Fazekas, G., Salamon, J., Dai, J., Bello, J., Dixon, S.: Computer-aided melody note transcription using the tony software: accuracy and efficiency. In: Proceedings of the First International Conference on Technologies for Music Notation and Representation (TENOR 2015) (2015)

  22. Narmour, E.: On the relationship of analytical theory to performance and interpretation. In: Solie, R., Narmour, E. (Eds.) Explorations of Music, the Arts, and Ideas: Essays in Honor of Leonard B. Meyer, pp 317–340. Pendragon Press, Stuyvesant (1987)

  23. Oliveira, J.L., Davies, M.E., Gouyon, F., Reis, L.P.: Beat tracking for multiple applications: a multi-agent system architecture with state recovery. IEEE Trans. Audio Speech Lang. Process. 20(10), 2696–2706 (2012)

    Article  Google Scholar 

  24. Roland, P.: The music encoding initiative (MEI). In: Proceedings of the First International Conference on Musical Applications Using XML, pp. 55–59 (2002)

  25. Rosenwald, L.: Theory, text-setting, and performance. J. Musicol. 11(1), 52–65 (1993)

    Article  Google Scholar 

  26. Schenker, H.: The Masterwork in Music: A Yearbook, vol. 1. Cambridge University Press, Cambridge (1925)

  27. Schmalfeldt, J.: On the relation of analysis to performance: Beethoven’s “Bagatelles” Op. 126, Nos. 2 and 5. J. Music Theory 29(1), 1–31 (1985)

    Article  Google Scholar 

  28. Tovey, D.A.: A Companion to Beethoven’s Pianoforte Sonatas. Associated Board, London (1935)

    Google Scholar 

  29. Viglianti, R.: The music addressability API: a draft specification for addressing portions of music notation on the web. In: Proceedings of the 3rd International Workshop on Digital Libraries for Musicology, pp. 57–60 (2016)

  30. Zhou, R., Reiss, J.D.: A real-time polyphonic music transcription system. In: Proceedings of the 4th Music Information Retrieval Evaluation EXchange (MIREX) (2008)

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Acknowledgements

This work was supported by the National Endowment for the Humanities’ Office of Digital Humanities under Grant [number HD-228966-15] and the Fonds de recherche du Québec – Société et culture.

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Authors and Affiliations

  1. School of Music, The Ohio State University, 1866 College Road, Columbus, OH, 43210, USA

    Johanna Devaney & Hubert Léveillé Gauvin

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  1. Johanna Devaney
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  2. Hubert Léveillé Gauvin
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Correspondence to Johanna Devaney.

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Devaney, J., Léveillé Gauvin, H. Encoding music performance data in Humdrum and MEI. Int J Digit Libr 20, 81–91 (2019). https://doi.org/10.1007/s00799-017-0229-3

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Keywords

  • Digital musicology
  • Music performance
  • Music encoding
  • Music representations