Sound source localization based on discrimination of cross-correlation functions

Abstract

Sound source localization plays a crucial role in many microphone arrays application, ranging from speech enhancement to human–computer interface in a reverberant noisy environment. The steered response power (SRP) using the phase transform (SRP-PHAT) method is one of the most popular modern localization algorithms. The SRP-based source localizers have been proved robust, however, the methods may fail to locate the sound source in adverse noise and reverberation conditions, especially when the direct paths to the microphones are unavailable. This paper proposes a localization algorithm based on discrimination of cross-correlation functions. The cross-correlation functions are calculated by the generalized cross-correlation phase transform (GCC-PHAT) method. Using cross-correlation functions, sound source location is estimated by one of the two classifiers: Naive-Bayes classifier and Euclidean distance classifier. Simulation results have demonstrated that the proposed algorithms provide higher localization accuracy than the SRP-PHAT algorithm in reverberant noisy environment.