Predicting Expressive Bow Controls for Violin and Viola

  • Lauren Jane Yu
  • Andrea Pohoreckyj Danyluk
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10198)


Though computational systems can simulate notes on a staff of sheet music, capturing the artistic liberties professional musicians take to communicate their interpretation of those notes is a much more difficult task. In this paper, we demonstrate that machine learning methods can be used to learn models of expressivity, focusing on bow articulation for violin and viola. First we describe a new data set of annotated sheet music with information about specific aspects of bow control. We then present experiments for building and testing predictive models for these bow controls, as well as analysis that includes both general metrics and manual examination.


Musical expression Machine learning Violin Viola Bow articulation 


  1. 1.
    Woody, R.H.: Learning expressivity in music performance: An exploratory study. Res. Stud. Music Educ. 14(1), 14–23 (2000)CrossRefGoogle Scholar
  2. 2.
    Randel, D.M. (ed.): The Harvard Dictionary of Music, 4th edn. The Belknap Press of Harvard University Press, Cambridge, London (2003)Google Scholar
  3. 3.
    Thippur, A., Askenfelt, A., Kjellström, H.: Probabilistic modeling of bowing gestures for gesture-based violin sound synthesis. In: Proceedings of Stockholm Music Acoustics Conference 2013, Stockholm, Sweden (2013)Google Scholar
  4. 4.
    Donington, R.: String Playing in Baroque Music. Faber Music Ltd., London (1977)Google Scholar
  5. 5.
    Cremer, L.: The Physics of the Violin. MIT Press, Cambridge (1983). translated by Allen, J.SGoogle Scholar
  6. 6.
    Schelleng, J.C.: The bowed string and the player. J. Acoust. Soc. Am. 53(1), 26–41 (1973)CrossRefGoogle Scholar
  7. 7.
    Juslin, P.N.: Five facets of musical expression: A psychologist’s perspective on music performance. Psychol. Music 31(3), 273–302 (2003)CrossRefGoogle Scholar
  8. 8.
    Marchini, M., Ramírez, R., Papiotis, P., Maestre, E.: The sense of ensemble: a machine learning approach to expressive performance modeling in string quartets. J. New Music Res. 43(3), 303–317 (2014)CrossRefGoogle Scholar
  9. 9.
    Neocleous, A., Ramírez, R., Pérez, A., Maestre, E.: Modeling emotions in violin audio recordings. In: ACM Workshop on Music and Machine Learning (ACM-MML), Firenze, Italy, pp. 17–20 (2010)Google Scholar
  10. 10.
    Percival, G., Bailey, N., Tzanetakis, G.: Physical modeling meets machine learning: Teaching bow control to a virtual violinist. In: Sound and Music Conference, Padova, Italy, July 2011Google Scholar
  11. 11.
    Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20(3), 273–297 (1995)zbMATHGoogle Scholar
  12. 12.
    Cristianini, N., Shawe-Taylor, J.: An Introduction to Support Vector Machines and Other Kernel-Based Learning Methods. Cambridge University Press, Cambridge (2000)CrossRefzbMATHGoogle Scholar
  13. 13.
    Rasmussen, C.E.: Gaussian processes in machine learning. In: Bousquet, O., Luxburg, U., Rätsch, G. (eds.) ML -2003. LNCS (LNAI), vol. 3176, pp. 63–71. Springer, Heidelberg (2004). doi: 10.1007/978-3-540-28650-9_4 CrossRefGoogle Scholar
  14. 14.
    Demoucron, M.: On the Control of Virtual Violins: Physical Modelling and Control of Bowed String Instruments. Ph.D. thesis, KTH, Sweden (2009)Google Scholar
  15. 15.
    Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The WEKA data mining software: An update. SIGKDD Explor. Newsl. 11(1), 10–18 (2009)CrossRefGoogle Scholar
  16. 16.
    Cover, T.M., Hart, P.E.: Nearest neighbor pattern classification. IEEE Trans. Inf. Theory 13(1), 21–27 (1967)CrossRefzbMATHGoogle Scholar
  17. 17.
    Alpaydin, E.: Introduction to Machine Learning, 3rd edn. MIT Press, Cambridge (2014)zbMATHGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Williams CollegeWilliamstownUSA

Personalised recommendations