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An Evaluation of Score Descriptors Combined with Non-linear Models of Expressive Dynamics in Music

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Discovery Science (DS 2015)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9356))

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Abstract

Expressive interpretation forms an important but complex aspect of music, in particular in certain forms of classical music. Modeling the relation between musical expression and structural aspects of the score being performed, is an ongoing line of research. Prior work has shown that some simple numerical descriptors of the score (capturing dynamics annotations and pitch) are effective for predicting expressive dynamics in classical piano performances. Nevertheless, the features have only been tested in a very simple linear regression model. In this work, we explore the potential of a non-linear model for predicting expressive dynamics. Using a set of descriptors that capture different types of structure in the musical score, we compare the predictive accuracies of linear and non-linear models. We show that, in addition to being (slightly) more accurate, non-linear models can better describe certain interactions between numerical descriptors than linear models.

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Notes

  1. 1.

    In the machine learning literature \(\{w_1,\dots ,w_{D_l}^{(l)}\}\) and \(w_{0}^{(l)}\) are respectively referred to as the set of weights and the bias of the l-th layer.

  2. 2.

    See Table 1 in [10] for an overview of the rules of the KTH model.

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Acknowledgment

This work is supported by European Union Seventh Framework Programme, through the Lrn2Cre8 (FET grant agreement no. 610859) and the PHENICX (grant agreement no. 601166) projects.

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Authors and Affiliations

  1. Austrian Research Institute for Artificial Intelligence, Vienna, Austria

    Carlos Eduardo Cancino Chacón & Maarten Grachten

Authors
  1. Carlos Eduardo Cancino Chacón
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  2. Maarten Grachten
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Corresponding author

Correspondence to Carlos Eduardo Cancino Chacón .

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Editors and Affiliations

  1. University of Ottawa, Ottawa, Ontario, Canada

    Nathalie Japkowicz

  2. Faculty of Computer Science, Dalhousie University, HALIFAX, Nova Scotia, Canada

    Stan Matwin

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Cancino Chacón, C.E., Grachten, M. (2015). An Evaluation of Score Descriptors Combined with Non-linear Models of Expressive Dynamics in Music. In: Japkowicz, N., Matwin, S. (eds) Discovery Science. DS 2015. Lecture Notes in Computer Science(), vol 9356. Springer, Cham. https://doi.org/10.1007/978-3-319-24282-8_6

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  • DOI: https://doi.org/10.1007/978-3-319-24282-8_6

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-24281-1

  • Online ISBN: 978-3-319-24282-8

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