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Movement Retraining using Real-time Feedback of Performance
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Shared periodic performer movements coordinate interactions in duo improvisations.

Tuomas Eerola1, Kelly Jakubowski1, Nikki Moran2

  • 1Department of Music, Durham University, Durham, UK.

Royal Society Open Science
|March 9, 2018
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Summary

This study reveals that body movement coordination predicts musical interaction. Computer vision quantified movement, showing high accuracy in predicting interaction moments in jazz and free improvisation.

Keywords:
coordinationentrainmentinteractionmusicperformancewavelet

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Area of Science:

  • Cognitive Science
  • Musicology
  • Computer Vision

Background:

  • Human interaction relies on coordinated, multimodal cues.
  • Music performance offers a complex model for analyzing temporally structured interactions.
  • Understanding visual coordination in ensembles is crucial for performance analysis.

Purpose of the Study:

  • To investigate if body movement coordination influences human judgments of interaction in musical duos.
  • To analyze interaction at multiple timescales in both pulsed and non-pulsed musical improvisation.
  • To develop and apply computational methods for quantifying visual coordination in music performance.

Main Methods:

  • Utilized two improvising duo datasets: jazz standards (pulsed) and free improvisations (non-pulsed).
  • Employed computer vision techniques to quantify performer body movements.
  • Manually annotated interaction bouts by experts and performers.
  • Predicted interaction bouts using movement and audio features, including cross-wavelet transform.

Main Results:

  • Over 80% of interaction bouts in non-pulsed duos were predicted by a broadband measure of movement energy.
  • A more complex model incorporating specific movement features was required for pulsed duos.
  • Cross-wavelet transform of co-performers' movements proved highly effective in predicting interaction.

Conclusions:

  • Body movement coordination is a significant predictor of perceived interaction in musical duos.
  • Computational analysis of movement can objectively measure visual coordination in ensemble performance.
  • Methods are adaptable to various musical contexts, ensemble types, and traditions.