Conventional approaches to acoustic source localization simply based on the signal received by microphone array, are often vulnerable to adverse acoustic conditions with low signal-to-noise ratio or high reverberation. Recent years, the approaches based on pattern recognition and machine learning technology are used to locate source in adverse acoustic environment. A weighted method based on Fisher discriminant theory is introduced to sound source localization based on Fisher Weighted Naive Bayes Classifier（FWNBC）. The eigenvector of each position is calculated by the cross-correlation function weighted by the Phase Transformation（PHAT）. Finally, the source location is estimated by using FWNBC. At the same time, experiments are carried out in a real location system to verify the performance of the improved algorithm. The experimental results show that compared with the Naive Bayes Classifier（NBC）, the FWNBC algorithm effectively improves the accuracy of sound source localization.

%U http://cea.ceaj.org/EN/10.3778/j.issn.1002-8331.1810-0409