Sound source localization through optimal peak association in reverberant environments
Hongyan Zhu, Zelin Li, Qi Cheng · 2017
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.