Xinwang Wan
College of Telecommunication and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing, 210003, China
Juan Liang
College of Telecommunication and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing, 210003, China
ABSTRACT
Sound source localization is very important in many microphone arrays application, ranging from speech enhancement to human-computer interface. The Steered Response Power (SRP) using the phase transform (SRP-PHAT) method has been proved robust but its performance degrades in highly reverberant noisy environment. The Naive-Bayes and Euclidean localization algorithms based on classification of cross-correlation functions outperform the SRP-PHAT in highly reverberant noisy environment. This study proposes the improved Naive-Bayes and Euclidean localization algorithms using principal eigenvector. Simulation results have demonstrated that the improved Naive-Bayes and Euclidean algorithms provide higher localization accuracy than the Naive-Bayes and Euclidean algorithms in reverberant noisy environment.
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How to cite this article
Xinwang Wan and Juan Liang, 2013. Improved Sound Source Localization Using Classifier in Reverberant Noisy Environment. Journal of Applied Sciences, 13: 4897-4901.
DOI: 10.3923/jas.2013.4897.4901
URL: https://scialert.net/abstract/?doi=jas.2013.4897.4901
DOI: 10.3923/jas.2013.4897.4901
URL: https://scialert.net/abstract/?doi=jas.2013.4897.4901
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