Joint speaker localization and array calibration using expectation-maximization

Ad hoc acoustic networks comprising multiple nodes, each of which consists of several microphones, are addressed. From the ad hoc nature of the node constellation, microphone positions are unknown. Hence, typical tasks, such as localization, tracking, and beamforming, cannot be directly applied. To tackle this challenging joint multiple speaker localization and array calibration task, we propose a novel variant of the expectation-maximization (EM) algorithm. The coordinates of multiple arrays relative to an anchor array are blindly estimated using naturally uttered speech signals of multiple concurrent speakers. The speakers’ locations, relative to the anchor array, are also estimated. The inter-distances of the microphones in each array, as well their orientations, are assumed known, which is a reasonable assumption for many modern mobile devices (in outdoor and in a several indoor scenarios). The well-known initialization problem of the batch EM algorithm is circumvented by an incremental procedure, also derived here. The proposed algorithm is tested by an extensive simulation study.