Adaptive beamforming combined with particle filtering for acoustic source localization

Abstract

While the main objective of adaptive Filter-and-Sum beamforming is to obtain an enhanced speech signal for subsequent processing like speech recognition, we show how speaker localization information can be derived from the filter coefficients. To increase localization accuracy, speaker tracking is performed by non-linear Bayesian state estimation, which is realized by sequential Monte Carlo methods. Improved acquisition and tracking performance was achieved even in highly reverberant environments, in comparison with both a Kalman Filter and a recently proposed Particle Filter operating on the output of a nonadaptive Delay-and-Sum beamformer.