%D 2016
%L eprints3119
%X 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.
%A Floriane Dardard
%A Giorgio Gnecco
%A Donald Glowinski
%N 1
%J ACM Transactions on Interactive Intelligent Systems (TiiS)
%T Automatic Classification of Leading Interactions in a String Quartet
%V 6
%I ACM