Distributed Marginalized Auxiliary Particle Filter for Speaker Tracking in Distributed Microphone Networks

摘要

In this paper, a distributed marginalized auxiliary particle filter (DMAPF) is proposed for speaker tracking in distributed microphone networks. After marginalizing the state-space model, the speaker's velocity and position are estimated using the distributed Kalman filter and the distributed auxiliary particle filter (APF), respectively. To overcome the adverse effects of noise and reverberation, a time difference of arrival selection scheme is presented to construct the local observation vector, based on the generalized cross-correlation function of the microphone pair signals at each node. Next, the multiple-hypothesis model is used as the local likelihood function of the DMAPF. Finally, the DMAPF is employed to estimate the time-varying positions of a moving speaker. The proposed method combines the strengths of the marginalized particle filter, APF, and distributed estimation. It can track the speaker successfully in noisy and reverberant environments. Moreover, it requires only local communication among neighboring nodes, and is scalable for speaker tracking. Experimental results reveal the validity of the proposed speaker tracking method.