Swarm Intelligence Based Particle Filter for Alternating Talker Localization and Tracking Using Microphone Arrays

We address the problem of localizing and tracking alternating (moving or stationary) talkers using microphone arrays in a room environment. One of the main challenges is the frequent (and possibly abrupt) change of talker positions, which requires the algorithm to capture the active talker rapidly. In addition, the presence of interference, background noise, and room reverberation degrades the tracking performance. We propose a new algorithm that jointly exploits the advantages of the particle filter (PF) and particle swarm intelligence. The PF is used as a general tracking framework, which incorporates a proposed alternating source-dynamic model for recursive estimation of talker position. Unlike the conventional PF, where particles operate independently in the particle sampling stage, the use of swarm intelligence allows particles to interact with each other, thereby improving convergence toward the active talker location. In addition, the memory mechanism in swarm intelligence allows particles to remain at their previous best-fit state estimate when signals are corrupted by interference, noise, and/or reverberation. Simulations and experiments were conducted to demonstrate the effectiveness of the proposed algorithm.