Experiments in acoustic source localization using sparse arrays in adverse indoors environments

In this paper we experiment with 2-D source localization in smart homes under adverse conditions using sparse distributed microphone arrays. We propose some improvements to deal with problems due to high reverberation, noise and use of a limited number of microphones. These consist of a pre-filtering stage for dereverberation and an iterative procedure that aims to increase accuracy. Experiments carried out in relatively large databases with both simulated and real recordings of sources in various positions indicate that the proposed method exhibits a better performance compared to others under challenging conditions while also being computationally efficient. It is demonstrated that although reverberation degrades localization performance, this degradation can be compensated by identifying the reliable microphone pairs and disposing of the outliers.