In this paper, we address the problem of sound source localization in reverberant environments. Time-delay estimation (TDE) methods are widely employed to locate sound sources based on the time differences of arrival (TDOAs) of signals received at different microphone pairs. In strong reverberations, the highest peak of the localization function is not necessarily from the true source resulting from the multi-path effect. Our previously proposed method based on the optimal peak association (OPA) aims to extract multiple peaks from the localization function for each microphone pair and find out the optimal association of TDOAs corresponding to the same sound source. However, due to the limitation of geometric configuration of microphones and possible missed detections, some microphone pairs fail to provide high-quality TDOA measurements. An improved OPA method is developed in this work based on the robust least squares which can determine the weights adaptively in terms of their respective observation accuracy. Experimental results demonstrate the superiority of the proposed method compared with the original OPA method in reverberant environments.

In this paper, we consider the source localization problem in which several microphones collaborate to locate an active sound source in a reverberant environment. Sound source localization (SSL) based on the Generalized Cross Correlation (GCC) function is widely studied for the past few decades. However, in a reverberant environment, the maximal peak of the GCC function does not necessarily correspond to the true source location due to the multipath effect. In this case, the traditional GCC-based method performs poorly. In this paper, by combining the information from all the available microphone pairs, we aim to seek a set of source-originated peaks rather than the maximal peaks. To achieve this, for each pair of microphones, multiple peaks of the GCC function indicating candidate TDOAs are extracted firstly. A graphic model is then constructed based on the extracted TDOAs from multiple microphone pairs, and the optimal association of peaks corresponding to true time delays can be obtained by optimizing the association cost function for the given set of peaks. Finally, the source location is estimated in the least square sense. Simulation results show the superior performance of the proposed approach compared with the traditional GCC-based localization algorithm.