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Interpretability of Music Classification as a Criterion for Evolutionary Multi-objective Feature Selection

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Evolutionary and Biologically Inspired Music, Sound, Art and Design (EvoMUSART 2015)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9027))

Abstract

The development of numerous audio signal characteristics led to an increase of classification performance for automatic categorisation of music audio recordings. Unfortunately, models built with such low-level descriptors lack of interpretability. Musicologists and listeners can not learn musically meaningful properties of genres, styles, composers, or personal preferences. On the other side, there are new algorithms for the mining of interpretable features from music data: instruments, moods and melodic properties, tags and meta data from the social web, etc. In this paper, we propose an approach how evolutionary multi-objective feature selection can be applied for a systematic maximisation of interpretability without a limitation to the usage of only interpretable features. We introduce a simple hypervolume based measure for the evaluation of trade-off between classification performance and interpretability and discuss how the results of our study may help to search for particularly relevant high-level descriptors in future.

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Author information

Authors and Affiliations

  1. Department of Computer Science, TU Dortmund, Dortmund, Germany

    Igor Vatolkin & Günter Rudolph

  2. Faculty of Statistics, TU Dortmund, Dortmund, Germany

    Claus Weihs

Authors
  1. Igor Vatolkin
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  2. Günter Rudolph
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  3. Claus Weihs
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Corresponding author

Correspondence to Igor Vatolkin .

Editor information

Editors and Affiliations

  1. University of Kent, Canterbury, United Kingdom

    Colin Johnson

  2. University of A Coruña, A Coruña, Spain

    Adrian Carballal

  3. University of Coimbra, Coimbra, Portugal

    João Correia

Appendix

Appendix

Fig. 1.
figure 1

Non-dominated fronts of feature subsets - Part I. Circles: C4.5, rectangles: random forest, diamonds: naive Bayes, triangles: support vector machines.

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Fig. 2.
figure 2

Non-dominated fronts of feature subsets - Part II.

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Vatolkin, I., Rudolph, G., Weihs, C. (2015). Interpretability of Music Classification as a Criterion for Evolutionary Multi-objective Feature Selection. In: Johnson, C., Carballal, A., Correia, J. (eds) Evolutionary and Biologically Inspired Music, Sound, Art and Design. EvoMUSART 2015. Lecture Notes in Computer Science(), vol 9027. Springer, Cham. https://doi.org/10.1007/978-3-319-16498-4_21

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  • DOI: https://doi.org/10.1007/978-3-319-16498-4_21

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-16497-7

  • Online ISBN: 978-3-319-16498-4

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