Multiple-Hypothesis Extended Particle Filter for Acoustic Source Localization in Reverberant Environments

Abstract

Particle filtering has been shown to be an effective approach to solving the problem of acoustic source localization in reverberant environments. In reverberant environment, the direct- arrival of the single source is accompanied by multiple spurious arrivals. Multiple-hypothesis model associated with these arrivals can be used to alleviate the unreliability often attributed to the acoustic source localization problem. Until recently, this multiple- hypothesis approach was only applied to bootstrap-based particle filter schemes. Recently, the extended Kalman particle filter (EPF) scheme which allows for an improved tracking capability was proposed for the localization problem. The EPF scheme utilizes a global extended Kalman filter (EKF) which strongly depends on prior knowledge of the correct hypotheses. Due to this, the extension of the multiple-hypothesis model for this scheme is not trivial. In this paper, the EPF scheme is adapted to the multiple-hypothesis model to track a single acoustic source in reverberant environments. Our work is supported by an extensive experimental study using both simulated data and data recorded in our acoustic lab. Various algorithms and array constellations were evaluated. The results demonstrate the superiority of the proposed algorithm in both tracking and switching scenarios. It is further shown that splitting the array into several sub-arrays improves the robustness of the estimated source location.