  It is known that adverse environments such as high reverberation and low
signal-to-noise ratio (SNR) pose a great challenge to indoor sound source
localization. To address this challenge, in this paper, we propose a sound
source localization algorithm based on probabilistic neural network, namely
Generalized cross correlation Classification Algorithm (GCA). Experimental
results for adverse environments with high reverberation time T60 up to 600ms
and low SNR such as -10dB show that, the average azimuth angle error and
elevation angle error by GCA are only 4.6 degrees and 3.1 degrees respectively.
Compared with three recently published algorithms, GCA has increased the
success rate on direction of arrival estimation significantly with good
robustness to environmental changes. These results show that the proposed GCA
can localize accurately and robustly for diverse indoor applications where the
site acoustic features can be studied prior to the localization stage.
