Extended Kalman filter with probabilistic data association for multiple non-concurrent speaker localization in reverberant environments

Abstract

Acoustic source localization and tracking (ASLT) in reverberant environments is a challenging task due to the multi-path propagation of acoustic waves. ASLT is often based on the use of a Kalman filter or a particle filter, with time-difference-of-arrival (TDOA) estimates used as measurements. In this work, we aim to track non-concurrent speakers by applying an extended Kalman filter (EKF) with probabilistic data association (PDA) that takes into account multiple measurements simultaneously. By using PDA, the inaccuracy of the measurements caused by room reflections and noise is explicitly taken into account. Unlike in typical approaches where the measurements consist of broadband TDOA estimates, the measurements in the proposed approach consist of multiple narrowband direction-of-arrival (DOA) estimates obtained from distributed microphone arrays. Experimental results demonstrate that incorporating PDA and using properly selected narrowband DOA estimates leads to a better tracking performance, as compared to the standard EKF with a single narrowband or broadband measurement.