SocialWeaver
Chengwen Luo, Mun Choon Chan · 2013
Understanding how people communicate with one another plays a very important role in many disciplines including social psychology, economics, marketing, and management science. This paper proposes and evaluates SocialWeaver, a sensing service running on smartphones that performs conversation clustering and builds conversation networks automatically. SocialWeaver uses a hybrid speaker classification scheme that exploits an adaptive histogram-based classifier to non-obtrusively bootstrap the in situ speaker model learning. The conversation clustering algorithm proposed is able to detect fine-grain conversation groups even if speakers are close together. Finally, to address energy constrain, a POMDP-based energy control scheme is incorporated.