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Visualizing music similarity: clustering and mapping 500 classical music composers

Author

Listed:
  • Patrick Georges

    (University of Ottawa)

  • Ngoc Nguyen

    (Western Kentucky University)

Abstract

This paper applies clustering techniques and multi-dimensional scaling (MDS) analysis to a 500 × 500 composers’ similarity/distance matrix. The objective is to visualize or translate the similarity matrix into dendrograms and maps of classical (European art) music composers. We construct dendrograms and maps for the Baroque, Classical, and Romantic periods, and a map that represents seven centuries of European art music in one single graph. Finally, we also use linear and non-linear canonical correlation analyses to identify variables underlying the dimensions generated by the MDS methodology.

Suggested Citation

  • Patrick Georges & Ngoc Nguyen, 2019. "Visualizing music similarity: clustering and mapping 500 classical music composers," Scientometrics, Springer;Akadémiai Kiadó, vol. 120(3), pages 975-1003, September.
    • Handle: RePEc:spr:scient:v:120:y:2019:i:3:d:10.1007_s11192-019-03166-0
    DOI: 10.1007/s11192-019-03166-0
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    References listed on IDEAS

    as
    1. Charles H. Smith & Patrick Georges & Ngoc Nguyen, 2015. "Statistical tests for ‘related records’ search results," Scientometrics, Springer;Akadémiai Kiadó, vol. 105(3), pages 1665-1677, December.
    2. J. Kruskal, 1964. "Multidimensional scaling by optimizing goodness of fit to a nonmetric hypothesis," Psychometrika, Springer;The Psychometric Society, vol. 29(1), pages 1-27, March.
    3. Patrick Georges, 2017. "Western classical music development: a statistical analysis of composers similarity, differentiation and evolution," Scientometrics, Springer;Akadémiai Kiadó, vol. 112(1), pages 21-53, July.
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    Cited by:

    1. Matteo Farnè, 2024. "Liszt’s Étude S.136 no.1: audio data analysis of two different piano recordings," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 18(3), pages 797-822, September.
    2. Patrick Georges & Aylin Seckin, 2022. "Music information visualization and classical composers discovery: an application of network graphs, multidimensional scaling, and support vector machines," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(5), pages 2277-2311, May.

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