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.”
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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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