Correspondence Analysis, Cross-Autocorrelation and Clustering in Polyphonic Music

  • Christelle CoccoEmail author
  • François Bavaud
Part of the Studies in Classification, Data Analysis, and Knowledge Organization book series (STUDIES CLASS)


This paper proposes to represent symbolic polyphonic musical data as contingency tables based upon the duration of each pitch for each time interval. Exploratory data analytic methods involve weighted multidimensional scaling, correspondence analysis, hierarchical clustering, and general autocorrelation indices constructed from temporal neighborhoods. Beyond the analysis of single polyphonic musical scores, the methods sustain inter-voices as well as inter-scores comparisons, through the introduction of ad hoc measures of configuration similarity and cross-autocorrelation. Rich musical patterns emerge in the related applications, and preliminary results are encouraging for clustering tasks.


Contingency Table Multiple Correspondence Analysis Exchange Matrix Music Piece Musical Score 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  1. 1.University of LausanneLausanneSwitzerland

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