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Auditory is a convenient and efficient way for Human-Robot Interaction, however implementing a sound source localization system based on TDOA method encounters many problems, such as noise of real environments, and resolution of nonlinear equations, switch between far field and near field and lack of microphones for geometric positioning localization method. In this paper, a new spectral weighting GCC-PHAT method is proposed to deal with noise. Furthermore, the time difference feature of sound source and its spatial distribution are analyzed. Based on prosperities of the distribution, a space grid matching (SGM) algorithm is proposed for localization step, which handles those problems that geometric positioning method faces effectively. Decision tree and valid feature detection algorithm are also proposed to reduce computational complexity and improve performance. Experiments are achieved in real environments on a mobile robot platform, in which 2016 sets of speech data are tested using four microphones in 3D space. More than 95% azimuth localization rate with error less than 5 degrees and approximate 90% horizontal distance localization rate are obtained.
Date of Conference: 25-30 Sept. 2011