Abstract
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.
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Notes
- 1.
The percentages of “off the string” notes in Wieniawski, Sibelius, and Bach No. 2 are 0, 2, and 3, respectively. As a result, learned models only predicted “on the string.”
References
Woody, R.H.: Learning expressivity in music performance: An exploratory study. Res. Stud. Music Educ. 14(1), 14–23 (2000)
Randel, D.M. (ed.): The Harvard Dictionary of Music, 4th edn. The Belknap Press of Harvard University Press, Cambridge, London (2003)
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)
Donington, R.: String Playing in Baroque Music. Faber Music Ltd., London (1977)
Cremer, L.: The Physics of the Violin. MIT Press, Cambridge (1983). translated by Allen, J.S
Schelleng, J.C.: The bowed string and the player. J. Acoust. Soc. Am. 53(1), 26–41 (1973)
Juslin, P.N.: Five facets of musical expression: A psychologist’s perspective on music performance. Psychol. Music 31(3), 273–302 (2003)
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)
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)
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 2011
Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20(3), 273–297 (1995)
Cristianini, N., Shawe-Taylor, J.: An Introduction to Support Vector Machines and Other Kernel-Based Learning Methods. Cambridge University Press, Cambridge (2000)
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
Demoucron, M.: On the Control of Virtual Violins: Physical Modelling and Control of Bowed String Instruments. Ph.D. thesis, KTH, Sweden (2009)
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)
Cover, T.M., Hart, P.E.: Nearest neighbor pattern classification. IEEE Trans. Inf. Theory 13(1), 21–27 (1967)
Alpaydin, E.: Introduction to Machine Learning, 3rd edn. MIT Press, Cambridge (2014)
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Yu, L.J., Danyluk, A.P. (2017). Predicting Expressive Bow Controls for Violin and Viola. In: Correia, J., Ciesielski, V., Liapis, A. (eds) Computational Intelligence in Music, Sound, Art and Design. EvoMUSART 2017. Lecture Notes in Computer Science(), vol 10198. Springer, Cham. https://doi.org/10.1007/978-3-319-55750-2_24
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DOI: https://doi.org/10.1007/978-3-319-55750-2_24
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