Speaker Tracking Based on Distributed Particle Filter and Iterative Covariance Intersection in Distributed Microphone Networks

Speaker tracking in distributed microphone networks is a challenging task due to the adverse effects of reverberation and noise. In this paper, a speaker tracking method based on distributed particle filter (DPF) and modified iterative covariance intersection (MICI) algorithm is proposed in distributed microphone networks. Specifically, the time difference of arrival (TDOA) of speech signals received by a pair of m icmp hones at each node is first estimated using the generalized cross-correlation function. Next, multiple TDOAs are considered as the local measurement via a choice strategy and the multiple-hypothesis model is used as the local likelihood function of the DPF. Finally, the MICI algorithm is proposed to fuse local estimates to implement a global consistent speaker tracking where the local estimates at individual nodes are allowed to be unknown cross-correlations. The proposed method can successfully track the speaker in reverberant and noisy environments, and it is robust to the node faults and suitable to track speaker in networks with the unknown number of nodes. Simulation results demonstrate that the proposed method has better performance over the existing methods when SNR < 10 dB. Real-world experiments reveal validity of the proposed method.