Nonlinear filtering for speaker tracking in noisy and reverberant environments

This paper addresses the problem of speaker tracking in a noisy and reverberant environment using time delay of arrival (TDOA) measurements at spatially distributed microphone pairs using sequential Monte Carlo methods to approximate the true filtering distribution with a set of samples.

Abstract

This paper addresses the problem of speaker tracking in a noisy and reverberant environment using time delay of arrival (TDOA) measurements at spatially distributed microphone pairs. The tracking problem is posed within a state-space estimation framework, and models are developed for the speaker motion and the likelihood of the speaker location in the light of the TDOA measurements. The resulting state-space model is nonlinear and nonGaussian, and consequently no closed-form solutions exist for the filtering distributions required to perform tracking. Here sequential Monte Carlo (SMC) methods are applied to approximate the true filtering distribution with a set of samples. The resulting tracking algorithm requires no triangulation, is computationally efficient, and can straightforwardly be extended to track multiple speakers.