Speaker Localization in Reverberant Noisy Environment Using Principal Eigenvector and Classifier

Abstract:

Sound source localization is essential 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 the algorithm may fail to locate the sound source in highly reverberant noisy environment. The Naive-Bayes localization algorithm based on classification of cross-correlation functions outperforms the SRP-PHAT in highly reverberant noisy environment. This paper proposes the improved Naive-Bayes localization algorithm using principal eigenvector. Simulation results have demonstrated that the proposed algorithm provides higher localization accuracy than the Naive-Bayes algorithm in reverberant noisy environment.

• Periodical: Applied Mechanics and Materials (Volumes 433-435)
• Pages: 416-419
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.433-435.416
• Online since: October 2013
• Authors: Xin Wang Wan*, Juan Liang
• Keywords: Cross-Correlation Function, Microphone Array, Naive-Bayes Classifier, Principal Eigenvector, Sound Source Localization

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