Skip to main content

Analysis of Mimed Violin Performance Movements of Neophytes

Patterns, Periodicities, Commonalities and Individualities

  • Conference paper
  • First Online:

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9617))

Abstract

Body movement and embodied knowledge play an important part in how we express and understand music. The gestures of a musician playing an instrument are part of a shared knowledge that contributes to musical expressivity by building expectations and influencing perception. In this study, we investigate the extent in which the movement vocabulary of violin performance is part of the embodied knowledge of individuals with no experience in playing the instrument. We asked people who cannot play the violin to mime a performance along an audio excerpt recorded by an expert. They do so by using a silent violin, specifically modified to be more accessible to neophytes. Preliminary motion data analyses suggest that, despite the individuality of each performance, there is a certain consistency among participants in terms of overall rhythmic resonance with the music and movement in response to melodic phrasing. Individualities and commonalities are then analysed using Functional Principal Component Analysis.

This is a preview of subscription content, log in via an institution.

Notes

  1. 1.

    http://www.optitrack.com.

  2. 2.

    L = Left; R = Right; F = Front; B = Back. A similar configuration can be found in [5].

  3. 3.

    Used to obtain an asymmetrical marker set, useful for marker identification and tracking. Not used for analysis.

  4. 4.

    http://www.reaper.fm.

  5. 5.

    http://www.qualisys.com.

  6. 6.

    https://cycling74.com.

  7. 7.

    The delay is plausibly due to the fact that the neophytes follow the audio recorded during the expert’s performance, therefore their movements slightly lag behind the ones of the expert.

References

  1. Altavilla, A., Caramiaux, B., Tanaka, A.: Towards gestural sonic affordances. In: Yeo, W., Lee, K., Sigman, A., H., J., Wakefield, G. (eds.) Proceedings of the International Conference on New Interfaces for Musical Expression, pp. 61–64. Graduate School of Culture Technology, KAIST, Daejeon, Republic of Korea (2013). http://nime2013.kaist.ac.kr/

  2. Amelynck, D., Maes, P.J., Martens, J.P., Leman, M.: Expressive body movement responses to music are coherent, consistent, and low dimensional. IEEE Trans. Cybern. 44(12), 2288–2301 (2014). http://www.ncbi.nlm.nih.gov/pubmed/25415938

  3. Bååth, R.: Estimating the distribution of sensorimotor synchronization data: a Bayesian hierarchical modeling approach. Behav. Res. Methods, 1–12 (2015). http://dx.doi.org/10.3758/s13428-015-0591-2

  4. Burger, B., Thompson, M.R., Luck, G., Saarikallio, S., Toiviainen, P.: Influences of rhythm-and timbre-related musical features on characteristics of music-induced movement. Front. Psychol. 4, 183 (2013). http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=3624091&tool=pmcentrez&rendertype=abstract

  5. Burger, B., Thompson, M.R., Luck, G., Saarikallio, S.H., Toiviainen, P.: Hunting for the beat in the body: on period and phase locking in music-induced movement. Front. Hum. Neurosci. 8, 1–16 (2014). http://www.frontiersin.org/Human_Neuroscience/10.3389/fnhum.2014.00903/abstract

    Article  Google Scholar 

  6. Burger, B., Toiviainen, P.: MoCap toolbox-a matlab toolbox for computational analysis of movement data. In: Proceedings of the Sound and Music Computing, pp. 172–178 (2013). https://jyx.jyu.fi/dspace/handle/123456789/42837

  7. Camurri, A., Mazzarino, B., Volpe, G.: Analysis of expressive gesture: the EyesWeb expressive gesture processing library. In: Camurri, A., Volpe, G. (eds.) GW 2003. LNCS (LNAI), vol. 2915, pp. 460–467. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  8. Camurri, A., Volpe, G.: Multimodal analysis of expressive gesture in music performance. In: Solis, J., Ng, K. (eds.) Musical Robots and Interactive Multimodal Systems. STAR, vol. 74, pp. 47–66. Springer, Heidelberg (2011)

    Chapter  Google Scholar 

  9. Clarke, E.F.: Ways of Listening: An Ecological Approach to the Perception of Musical Meaning. Oxford University Press, New York (2005)

    Book  Google Scholar 

  10. Coorevits, E., Moelants, D., Maes, P.J., Leman, M.: Studying the effect of tempo on music performance: a multimodal approach. In: 9th Conference on Interdisciplinary Musicology, CIM 2014, Berlin (2014)

    Google Scholar 

  11. Dahl, L.: Triggering sounds from discrete air gestures: what movement feature has the best timing? In: Caramiaux, B., Tahiroglu, K., Fiebrink, R., Tanaka, A. (eds.) Proceedings of the International Conference on New Interfaces for Musical Expression, pp. 201–206. Goldsmiths, University of London, London (2014). http://www.nime.org/proceedings/2014/nime2014_514.pdf

  12. Dempster, W.T., Gaughran, G.R.L.: Properties of body segments based on size and weight. Am. J. Anat. 120(1), 33–54 (1967). doi:10.1002/aja.1001200104

    Article  Google Scholar 

  13. Gibson, E.J.: Where is the information for affordances? Ecol. Psychol. 12(1), 53–56 (2000)

    Article  Google Scholar 

  14. Gibson, J.J.: The theory of affordances. In: Perceiving, Acting, and Knowing, vol. Perceiving, pp. 127–142 (332) (1977)

    Google Scholar 

  15. Glowinski, D., Dardard, F., Gnecco, G., Piana, S., Camurri, A.: Expressive non-verbal interaction in a string quartet: an analysis through head movements. J. Multimodal User Interfaces 9(1), 55–68 (2014). http://link.springer.com/10.1007/s12193-014-0154-3

    Article  Google Scholar 

  16. Godøy, R.I., Haga, E., Jensenius, A.R.: Playing “air instruments”: mimicry of sound-producing gestures by novices and experts. In: Gibet, S., Courty, N., Kamp, J.-F. (eds.) GW 2005. LNCS (LNAI), vol. 3881, pp. 256–267. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  17. Godøy, R.I., Leman, M. (eds.): Musical Gestures: Sound, Movement, and Meaning. Routledge, Abingdon (2010)

    Google Scholar 

  18. Gritten, A.: Resonant listening. Perform. Res. 15(3), 115–122 (2010). http://www.tandfonline.com/doi/abs/10.1080/13528165.2010.527221

    Article  Google Scholar 

  19. Gritten, A., King, E. (eds.): Music and Gesture. Ashgate, Farnham (2006)

    Google Scholar 

  20. Jensenius, A.R., Skogstad, S.A., Nymoen, K., Tørresen, J., Høvin, M.E.: Reduced displays of multidimensional motion capture data sets of musical performance. In: Proceedings of ESCOM 2009: 7th Triennial Conference of the European Society for the Cognitive Sciences of Music (2009)

    Google Scholar 

  21. Leman, M.: Embodied Music Cognition and Mediation Technology. MIT Press, Cambridge (2008)

    Google Scholar 

  22. Leman, M., Lesaffre, M., Nijs, L., Deweppe, A.: User-oriented studies in embodied music cognition research. Musicae Sci. 14(2), 203–223 (2010). http://msx.sagepub.com/content/14/2_suppl/203.abstract

    Article  Google Scholar 

  23. MacRitchie, J., Buck, B., Bailey, N.J.: Inferring musical structure through bodily gestures. Musicae Sci. 17(1), 86–108 (2013). http://msx.sagepub.com/lookup/doi/10.1177/1029864912467632

    Article  Google Scholar 

  24. Menin, D., Schiavio, A.: Rethinking musical affordances. Avant III(2), 201–215 (2012)

    Google Scholar 

  25. Naveda, L., Leman, M.: The spatiotemporal representation of dance and music gestures using topological gesture analysis (TGA). Music Percept. 28(1), 93–111 (2010)

    Article  Google Scholar 

  26. Ramsay, J.O.: Functional Data Analysis. Wiley, Hoboken (2006)

    Google Scholar 

  27. Visi, F., Coorevits, E., Miranda, E., Leman, M.: Effects of different bow stroke styles on body movements of a viola player: an exploratory study. In: Proceedings of the Joint ICMC–SMC–2014 Conference, Athens, Greece (2014)

    Google Scholar 

  28. Visi, F., Schramm, R., Miranda, E.: Gesture in performance with traditional musical instruments and electronics: use of embodied music cognition and multimodal motion capture to design gestural mapping strategies. In: Proceedings of the 2014 International Workshop on Movement and Computing, MOCO 2014. ACM, Paris (2014). http://dl.acm.org/citation.cfm?id=2618013

Download references

Acknowledgements

The authors would like to thank Alexander Refsum Jensenius and all the members of the fourMs - Music, Mind, Motion, Machines research group at the University of Oslo, Norway, for their hospitality and knowledgeable support. Special thanks to all the participants of the study and to Pierre-Emmanuel Largeron for his valuable input.

This study was partially realised under the FWO-project “Foundations of expressive timing control in music”.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Federico Visi .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing Switzerland

About this paper

Cite this paper

Visi, F., Coorevits, E., Schramm, R., Miranda, E.R. (2016). Analysis of Mimed Violin Performance Movements of Neophytes. In: Kronland-Martinet, R., Aramaki, M., Ystad, S. (eds) Music, Mind, and Embodiment. CMMR 2015. Lecture Notes in Computer Science(), vol 9617. Springer, Cham. https://doi.org/10.1007/978-3-319-46282-0_6

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-46282-0_6

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-46281-3

  • Online ISBN: 978-3-319-46282-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics