The Clustering of Expressive Timing Within a Phrase in Classical Piano Performances by Gaussian Mixture Models

  • Shengchen Li
  • Dawn A. A. Black
  • Mark D. Plumbley
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9617)

Abstract

In computational musicology research, clustering is a common approach to the analysis of expression. Our research uses mathematical model selection criteria to evaluate the performance of clustered and non-clustered models applied to intra-phrase tempo variations in classical piano performances. By engaging different standardisation methods for the tempo variations and engaging different types of covariance matrices, multiple pieces of performances are used for evaluating the performance of candidate models. The results of tests suggest that the clustered models perform better than the non-clustered models and the original tempo data should be standardised by the mean of tempo within a phrase.

Keywords

Intra-phrase tempo Model analysis Classical piano performance Model selection criteria 

References

  1. 1.
    Balakirev, M.: Islamey, Op. 18. D. Rahter, Hamburg (1902). http://imslp.org/wiki/Islamey,_Op.18_(Balakirev,_Mily)
  2. 2.
    Burnham, K.P., Anderson, D.R.: Model Selection and Multimodel Inference - A Practical Information-Theoretic Approach, 2nd edn. Springer, Berlin (2002)MATHGoogle Scholar
  3. 3.
    Cambouropoulos, E., Dixon, S., Goebl, W., Widmer, G.: Human preferences for tempo smoothness. In: Proceedings of the VII International Symposium on Systematic and Comparative Musicology and III International Conference on Cognitive Musicology, pp. 18–26 (2001)Google Scholar
  4. 4.
    Claeskens, G., Hjort, N.L.: Model Selection and Model Averaging. Cambridge University Press, Cambridge (2008)CrossRefMATHGoogle Scholar
  5. 5.
    Davies, M.E.P., Plumbley, M.D.: Context-dependent beat tracking of musical audio. IEEE Trans. Audio Speech Lang. Process. 15(3), 1009–1020 (2007)CrossRefGoogle Scholar
  6. 6.
    Degara, N., Rua, E.A., Pena, A., Torres-Guijarro, S., Davies, M.E.P., Plumbley, M.D.: Reliability-informed beat tracking of musical signals. IEEE Trans. Audio Speech Lang. Process. 20(1), 290–301 (2011)CrossRefGoogle Scholar
  7. 7.
    Desain, P., Honing, H.: Does expressive timing in music performance scale proportionally with tempo? Psychol. Res. 56, 285–292 (1994)CrossRefGoogle Scholar
  8. 8.
    Dixon, S.: Automatic extraction of tempo and beat from expressive performances. J. New Music Res. 30(1), 39–58 (2001)CrossRefGoogle Scholar
  9. 9.
    Franz, F.: Metronome Techniques: Being A Very Brief Account of the History and Use of the Metronome with Many Practical Applications for the Musician. Printing-Office of The Yale University Press, New Haven (1947)Google Scholar
  10. 10.
    Grosche, P., Muller, M., Sapp, C.S.: What makes beat tracking difficult? A case study on Chopin Mazurkas. In: Proceedings of the International Conference on Music Information Retrieval (ISMIR), pp. 649–654 (2010)Google Scholar
  11. 11.
    Madsen, S.T., Widmer, G.: Exploring pianist performance styles with evolutionary string matching. Int. J. Artif. Intell. Tools 15(4), 495–514 (2006)CrossRefGoogle Scholar
  12. 12.
    Murphy, K.P.: Machine Learning: A Probabilistic Perspective. The MIT Press, Cambridge (2012)MATHGoogle Scholar
  13. 13.
    Repp, B.H.: Diversity and commonality in music performance: an analysis of timing microstructure in Schumann’s “Träumerei”. J. Acoust. Soc. Am. 92, 2546–2568 (1993)CrossRefGoogle Scholar
  14. 14.
    Repp, B.H.: Expressive timing in Schumann’s Träumerei: an analysis of performances by graduate student pianists. J. Acoust. Soc. Am. 5, 2413–2427 (1995)CrossRefGoogle Scholar
  15. 15.
    Repp, B.H.: Quantitative effects of global tempo on expressive timing in music performance: some percepeptual evidence. Music Percept. Interdisc. J. 13, 39–57 (1995)CrossRefGoogle Scholar
  16. 16.
    Repp, B.H.: A microcosm of musical expression. I. Quantitave analysis of pianists’ timing in the initial measures of Chopin’s Etude in E major. The. J. Acoust. Soc. Am. 104, 1085–1100 (1998)CrossRefGoogle Scholar
  17. 17.
    Sapp, C.: Comparative analysis of multiple musical performances. In: Proceedings of the International Conference on Music Information Retrieval (ISMIR), pp. 497–500 (2007)Google Scholar
  18. 18.
    Sapp, C.: Hybrid numeric/rank similarity metrics for musical performance analysis. In: Proceedings of the International Conference on Music Information Retrieval (ISMIR), pp. 501–506 (2008)Google Scholar
  19. 19.
    Spearman, C.: The proof and measurement of association between two things. Am. J. Psychol. 15(1), 72–101 (1904)CrossRefGoogle Scholar
  20. 20.
    Spiegel, M.R., Stephens, L.J.: Schuaum’s Outlines: Statistics. McGraw-Hill Education, New York (2011)Google Scholar
  21. 21.
    Spiro, N., Gold, N., Rink, J.: Plus ça change: Analyzing performances of Chopin’s Mazurka Op. 24 No. 2. In: Proceedings of International Conference on Music Perception and Cognition (ICMPC), pp. 418–427 (2008)Google Scholar
  22. 22.
    Spiro, N., Gold, N., Rink, J.: The form of performance: analyzing pattern distribution in select recordings of Chopin’s Mazurka op. 24 no. 2. Musicae Sci. 14(2), 23–55 (2010)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Shengchen Li
    • 1
  • Dawn A. A. Black
    • 2
  • Mark D. Plumbley
    • 3
  1. 1.Queen Mary University of LondonLondonUK
  2. 2.RadioscapeLondonUK
  3. 3.University of SurreyGuildfordUK

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