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Automatic Classification of Leading Interactions in a String Quartet

Automatic Classification of Leading Interactions in a String Quartet The aim of the present work is to analyze automatically the leading interactions between the musicians of a string quartet, using machine-learning techniques applied to nonverbal features of the musicians behavior, which are detected through the help of a motion-capture system. We represent these interactions by a graph of influence of the musicians, which displays the relations is following and is not following with weighted directed arcs. The goal of the machine-learning problem investigated is to assign weights to these arcs in an optimal way. Since only a subset of the available training examples are labeled, a semisupervised support vector machine is used, which is based on a linear kernel to limit its model complexity. Specific potential applications within the field of human-computer interaction are also discussed, such as e-learning, networked music performance, and social active listening. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png ACM Transactions on Interactive Intelligent Systems (TiiS) Association for Computing Machinery

Automatic Classification of Leading Interactions in a String Quartet

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References (41)

Publisher
Association for Computing Machinery
Copyright
Copyright © 2016 ACM
ISSN
2160-6455
eISSN
2160-6463
DOI
10.1145/2818739
Publisher site
See Article on Publisher Site

Abstract

The aim of the present work is to analyze automatically the leading interactions between the musicians of a string quartet, using machine-learning techniques applied to nonverbal features of the musicians behavior, which are detected through the help of a motion-capture system. We represent these interactions by a graph of influence of the musicians, which displays the relations is following and is not following with weighted directed arcs. The goal of the machine-learning problem investigated is to assign weights to these arcs in an optimal way. Since only a subset of the available training examples are labeled, a semisupervised support vector machine is used, which is based on a linear kernel to limit its model complexity. Specific potential applications within the field of human-computer interaction are also discussed, such as e-learning, networked music performance, and social active listening.

Journal

ACM Transactions on Interactive Intelligent Systems (TiiS)Association for Computing Machinery

Published: Mar 9, 2016

Keywords: Automated analysis of nonverbal behavior

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