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Drum Rhythm Spaces: From Global Models to Style-Specific Maps

  • Daniel Gómez-MarínEmail author
  • Sergi Jordà
  • Perfecto Herrera
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11265)

Abstract

This paper presents two experiments carried out to find rhythm descriptors that allow the organization of drum patterns in spaces resembling subjects similarity sensations. We revisit rhythm spaces published by Alf Gabrielsson in 1973, based on subject similarity ratings of drum rhythms from an early drum machine, and construct a new rhythm space based on similarity judgments using contemporary electronic dance music (EDM) patterns. We observe how a specific set of descriptors can be used to reconstruct both Gabrielsson’s and the new EDM space, suggesting the descriptors capture drum similarity sensations in very different contexts. The set of descriptors and the methods employed are explained with detail and the possibility of having method for organizing rhythm patterns automatically is discussed.

Keywords

Rhythm space Electronic Dance Music (EDM) Drum patterns Rhythm representations Music cognition Conceptual maps 

Notes

Acknowledgments

This research has been partially supported by the EU funded GiantSteps project (http://www.giantsteps-project.eu) (FP7-ICT-2013-10 Grant agreement nr 610591).

References

  1. 1.
    Butler, M.J.: Unlocking the Groove: Rhythm, Meter, and Musical Design in Electronic Dance Music. Indiana University Press, Bloomington (2006)Google Scholar
  2. 2.
    Cao, E., Lotstein, M., Johnson-Laird, P.N.: Similarity and families of musical rhythms. Music. Percept. Interdiscip. J. 31(5), 444–469 (2014)CrossRefGoogle Scholar
  3. 3.
    Esparza, T.M., Bello, J.P., Humphrey, E.J.: From genre classification to rhythm similarity: computational and musicological insights. J. New Music. Res. 44(1), 39–57 (2015)CrossRefGoogle Scholar
  4. 4.
    Fujii, S., Schlaug, G.: The harvard beat assessment test (H-BAT): a battery for assessing beat perception and production and their dissociation. Front. Hum. Neurosci. 7, 771 (2013)CrossRefGoogle Scholar
  5. 5.
    Gärdenfors, P.: Conceptual Spaces: The Geometry of Thought, 1st edn. The MIT Press, Cambridge (2000)Google Scholar
  6. 6.
    Gabrielsson, A.: Similarity ratings and dimension analyses of auditory rhythm patterns: I. Scand. J. Psychol. 14, 138–160 (1973)CrossRefGoogle Scholar
  7. 7.
    Gabrielsson, A.: Similarity ratings and dimension analyses of auditory rhythm patterns: II. Scand. J. Psychol. 14(3), 161–176 (1973)CrossRefGoogle Scholar
  8. 8.
    Gómez-Marín, D., Jordà, S., Herrera, P.: Strictly rhythm: exploring the effects of identical regions and meter induction in rhythmic similarity perception. In: Kronland-Martinet, R., Aramaki, M., Ystad, S. (eds.) CMMR 2015. LNCS, vol. 9617, pp. 449–463. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-46282-0_29CrossRefGoogle Scholar
  9. 9.
    Gómez-Marín, D., Jordà, S., Herrera, P.: Pad and sad: two awareness-weighted rhythmic similarity distances. In: 16th International Society for Music Information Retrieval Conference ISMIR, Málaga (2015)Google Scholar
  10. 10.
    Gouyon, F., et al.: Evaluating rhythmic descriptors for musical genre classification. In: Proceedings of the AES 25th International Conference (2004)Google Scholar
  11. 11.
    Grey, J.M.: Multidimensional perceptual scaling of musical timbres. J. Acoust. Soc. Am. 61(5), 1270–1277 (1977)CrossRefGoogle Scholar
  12. 12.
    Hove, M.J., Marie, C., Bruce, I.C., Trainor, L.J.: Superior time perception for lower musical pitch explains why bass-ranged instruments lay down musical rhythms. Proc. Natl. Acad. Sci. 111(28), 10383–10388 (2014)CrossRefGoogle Scholar
  13. 13.
    Johnson-Laird, P.N.: Rhythm and meter: a theory at the computational level. Psychomusicology J. Res. Music. Cogn. 10(2), 88–106 (1991)CrossRefGoogle Scholar
  14. 14.
    Krumhansl, C.L.: The psychological representation of musical pitch in a tonal context. Cogn. Psychol. 11(3), 346–374 (1979)CrossRefGoogle Scholar
  15. 15.
    Longuet-Higgins, H.C., Lee, C.S.: The rhythmic interpretation of monophonic music. Music. Percept. Interdiscip. J. 1(4), 424–441 (1984)CrossRefGoogle Scholar
  16. 16.
    Palmer, C., Krumhansl, C.L.: Mental representations for musical meter. J. Exp. Psychology. Hum. Percept. Perform. 16(4), 728–741 (1990)Google Scholar
  17. 17.
    Patel, A.D., Iversen, J.R.: The evolutionary neuroscience of musical beat perception: the action simulation for auditory prediction (ASAP) hypothesis. Front. Syst. Neurosci. 8, 57 (2014)CrossRefGoogle Scholar
  18. 18.
    Pedregosa, F., et al.: Scikit-learn: machine learning in python. J. Mach. Learn. Res. 12, 2825–2830 (2011)Google Scholar
  19. 19.
    Shepard, R.N.: The analysis of proximities: Multidimensional scaling with an unknown distance function II. Psychometrika 27(3), 219–246 (1962)MathSciNetCrossRefGoogle Scholar
  20. 20.
    Shoben, E.J., Ross, B.H.: 7. Structure and process in cognitive psychology using multidimensional scaling and related techniques. In: Ronning, R.R., Glover, J.A., Conoley, J.C., Witt, J.C. (eds.) The Influence of Cognitive Psychology on Testing, Hillsdale, NJ. Lawrence Erlbaum Associates (1987)Google Scholar
  21. 21.
    Witek, M.A.G., Clarke, E.F., Wallentin, M., Kringelbach, M.L., Vuust, P.: Syncopation, body-movement and pleasure in groove music. PLoS ONE 9(4), e94446 (2014)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Daniel Gómez-Marín
    • 1
    Email author
  • Sergi Jordà
    • 1
  • Perfecto Herrera
    • 1
  1. 1.Music Technology GroupUniversitat Pompeu FabraBarcelonaSpain

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