Accelerated speech source localization via a hierarchical search of steered response power

Accurate and fast localization of multiple speech sound sources is a problem that is of significant interest in applications such as conferencing systems. Recently, approaches that are based on search for local peaks of the steered response power are becoming popular, despite their known computational expense. Based on the observation that the wavelengths of the sound from a speech source are comparable to the dimensions of the space being searched and that the source is broadband, we have developed an efficient search algorithm. Significant speedups are achieved by using coarse-to-fine strategies in both space and frequency. We present applications of the search algorithm to speed up simple delay-and-sum beamforming and steered response power phase-transform weighted (SRP-PHAT) source localization algorithms. A systematic series of comparisons with previous algorithms are made that show that the technique is much faster, robust, and accurate. The performance of the algorithm can be further improved by using constraints from computer vision.