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Composer Classification Models for Music-Theory Building

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Computational Music Analysis

Abstract

The task of recognizing a composer by listening to a musical piece used to be reserved for experts in music theory. The problems we address here are, first, that of constructing an automatic system that is able to distinguish between music written by different composers; and, second, identifying the musical properties that are important for this task. We take a data-driven approach by scanning a large database of existing music and develop five types of classification model that can accurately discriminate between three composers (Bach, Haydn and Beethoven). More comprehensible models, such as decision trees and rulesets, are built, as well as black-box models such as support vector machines. Models of the first type offer important insights into the differences between composer styles, while those of the second type provide a performance benchmark.

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Authors and Affiliations

  1. ANT/OR, University of Antwerp Operations Research Group, Antwerp, Belgium

    Dorien Herremans & Kenneth Sörensen

  2. Applied Data Mining Research Group, University of Antwerp, Antwerp, Belgium

    David Martens

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  1. Dorien Herremans
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  2. David Martens
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  3. Kenneth Sörensen
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Correspondence to Dorien Herremans .

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Editors and Affiliations

  1. Dept. of Arch., Design & Media Tech, Aalborg University, Aalborg, Denmark

    David Meredith

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Herremans, D., Martens, D., Sörensen, K. (2016). Composer Classification Models for Music-Theory Building. In: Meredith, D. (eds) Computational Music Analysis. Springer, Cham. https://doi.org/10.1007/978-3-319-25931-4_14

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  • DOI: https://doi.org/10.1007/978-3-319-25931-4_14

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