Distributed Kalman filter-based speaker tracking in microphone array networks

Abstract

Using a microphone array network, a speaker tracking method based on distributed Kalman filter (DKF) in a noisy and reverberant environment is proposed. Firstly, the time delay of arrival (TDOA) in each microphone pair is estimated by the generalized cross-correlation (GCC) method. Next, the Langevin model is used as state equation to model the speaker’s movement, meanwhile the measurement equations with true TDOA are deduced by linearizing the TDOA model. Finally, the moving speaker’s positions are estimated by distributed Kalman filtering in a microphone array network. The proposed method is scalable. It can obtain a trajectory of the speaker’s movement smoothly with excellent tracking accuracy. Simulation results verify the effectiveness of the proposed method.