Improvement of speech source localization in noisy environment using overcomplete rational-dilation wavelet transforms

The generalized cross-correlation using the phase transform prefilter remains popular for the estimation of time-differences-of-arrival. However it is not robust to noise and as a consequence, the performance of direction-of-arrival algorithms is often degraded under low signal-to-noise condition. We propose to address this problem through the use of a wavelet-based speech enhancement technique since the wavelet transform can achieve good denoising performance. The over-complete rational-dilation wavelet transform is then exploited to effectively process speech signals due to its higher frequency resolution. In addition, we exploit the joint distribution of the speech in the wavelet domain and develop a novel local noise variance estimator based on the bivariate shrinkage function. As will be shown, our proposed algorithm achieves good direction-of-arrival performance in the presence of noise.