Classification of Acoustic Maps to Determine Speaker Position and Orientation from a Distributed Microphone Network

Acoustic maps created on the basis of the signals acquired by distributed networks of microphones allow to identify position and orientation of an active talker in an enclosure. In adverse situations of high background noise, high reverberation or unavailability of direct paths to the microphones, localization may fail. This paper proposes a novel approach to talker localization and estimation of head orientation based on the classification of global coherence field (GCF) or oriented GCF maps. Preliminary experiments with data obtained by simulated propagation as well as with data acquired in a real room show that the match with precalculated map models provides a robust behavior in adverse conditions.