Experimental comparison of particle filtering algorithms for acoustic source localization in a reverberant room

Traditional acoustic source localization techniques attempt to determine the current location of an acoustic source from data obtained at an array of sensors during the current time only. Recently, state-space methods have been proposed that use particle filters to perform recursive estimation of the current source location using all previous data. We present an overview of these particle filter algorithms, and formulate performance measures for determining their ability to track a moving source. We present results of experiments using reverberant data recorded in a real room, and show that steered beamforming methods have improved performance over GCC-based approaches.