Indoor Sound Source Localization With Probabilistic Neural Network

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 (SSL). To address this challenge, in this paper, we propose an SSL algorithm based on a probabilistic neural network, namely a generalized cross-correlation classification algorithm (GCA). Experimental results for adverse environments with high reverberation time T_{60} up to 600 ms and low SNR such as -10 dB show that the average azimuth angle error and elevation angle error by GCA are only 4.6° and 3.1°, 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.