Analysing musical performance through functional data analysis: rhythmic structure in Schumann's Traumerei

Josue Almansa*, Pedro Delicado

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

8 Citations (Scopus)


Functional data analysis (FDA) is a relatively new branch of statistics devoted to describing and modelling data that are complete functions. Many relevant aspects of musical performance and perception can be understood and quantified as dynamic processes evolving as functions of time. In this paper, we show that FDA is a statistical methodology well suited for research into the field of quantitative musical performance analysis. To demonstrate this suitability, we consider tempo data for 28 performances of Schumann's Traumerei and analyse them by means of functional principal component analysis (one of the most powerful descriptive tools included in FDA). Specifically, we investigate the commonalities and differences between different performances regarding (expressive) timing, and we cluster similar performances together. We conclude that musical data considered as functional data reveal performance structures that might otherwise go unnoticed.

Original languageEnglish
Article number911513311
Pages (from-to)207-225
Number of pages19
JournalConnection Science
Issue number2-3
Publication statusPublished - 2009
Externally publishedYes


  • commonalities
  • cluster analysis
  • diversity
  • local polynomial smoothing
  • principal component analysis
  • tempo
  • timing

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