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Proceedings chapter
English

Motion analysis and classification of salsa dance using music-related motion features

Presented at Limassol (Cyprus), November 2018
PublisherACM
Publication date2018
Abstract

Learning couple dance such as Salsa is a challenge for modern human as it requires to assimilate and understand correctly all the required parameters. In this paper, we propose a set of music-related motion features (MMF) allowing to describe, analyse and classify salsa dancer couple in their respective learning state (beginner, intermediate and expert). These dance qualities have been proposed from a systematic review of papers cross linked with interviews from teacher and professionals in the field of social dance. We investigated how to extract these MMF from musical data and 3D movements of dancers in order to propose a new algorithm to compute them. For the presented study, a motion capture database (SALSA) has been recorded of 26 different couples with varying skill levels dancing on 10 different tempos (260 clips). Each recorded clips contains a basic steps sequence and an extended improvisation sequence during two minutes in total at 120 frame per second. We finally use our proposed algorithm to analyse and classify these 26 couples in three learning levels, which validates some proposed music-related motion features and give insights on others.

Keywords
  • Computing methodologies
  • Artificial intelligence
  • Computer vision
  • Image and video acquisition
  • Motion capture
  • Computer graphics
  • Animation
  • Motion processing
  • Machine learning
  • Machine learning approaches
  • Modeling and simulation
  • Model development and analysis
Citation (ISO format)
SENECAL, Simon, NIJDAM, Niels Alexander, MAGNENAT THALMANN, Nadia. Motion analysis and classification of salsa dance using music-related motion features. In: Proceedings of the 11th annual international conference on motion, interaction, and games, mig ’18. Limassol (Cyprus). [s.l.] : ACM, 2018. p. 1–10. doi: 10.1145/3274247.3274514
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Proceedings chapter (Published version)
accessLevelRestricted
Identifiers
ISBN978-1-4503-6015-9
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