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Measuring ensemble interdependence in a string quartet through analysis of multidimensional performance data.

Panos Papiotis1, Marco Marchini1, Alfonso Perez-Carrillo2

  • 1Music Technology Group, Department of Information and Communication Technologies, Universitat Pompeu Fabra Barcelona, Spain.

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Summary

This study introduces a computational method to measure musician interactions in string quartets. Quantifying interdependence in intonation, dynamics, timbre, and tempo accurately distinguishes ensemble playing.

Keywords:
ensemble performancegranger causalityinterdependencemotion capturemutual informationnonlinear couplingsignal processingstring quartet

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Area of Science:

  • Music Performance Science
  • Computational Musicology
  • Ensemble Dynamics

Background:

  • Musicians in ensembles like string quartets exhibit complex, simultaneous interactions to achieve shared aesthetic goals.
  • Understanding and quantifying these interpersonal dynamics is crucial for advancing ensemble performance research.

Purpose of the Study:

  • To develop and evaluate a computational approach for measuring the degree of interdependence among musicians in a string quartet.
  • To assess the effectiveness of different interdependence estimation methods in analyzing performance data.

Main Methods:

  • Recorded string quartet performances under solo and ensemble conditions, capturing audio and bowing motion data.
  • Extracted time-series features representing Intonation, Dynamics, Timbre, and Tempo.
  • Applied four interdependence estimation methods (two linear, two nonlinear) to quantify musician interactions.

Main Results:

  • The computational approach successfully discriminated between solo and ensemble conditions by quantifying interdependence.
  • Nonlinear interdependence estimation methods generally outperformed linear methods across various performance dimensions.
  • Solo recordings served as a baseline to compare and analyze interdependence levels in ensemble playing.

Conclusions:

  • Quantifying interdependence provides a viable method for analyzing ensemble musical performance.
  • The developed approach offers insights into how musicians coordinate across multiple performance dimensions.
  • Future work can expand this methodology for broader applications in music performance analysis and training.