Alpha-stable robust modeling of background noise for enhanced sound source localization

In this paper we address the problem of sound source localization in the presence of impulsive noise for application in immersive telepresence and teleconferencing. Traditional Gaussian modeling of noise signals fails when the signals exhibit impulsive behavior. A new model is used, namely the Symmetric a-Stable (SaS), which can better account for the outliers that exist in real-world signals. Real data is used to compare the performance of both the Gaussian and the a-stable models. We demonstrate that the astable model gives a much better approximation to the noise signal than the Gaussian model. Furthermore, we study the problem of Time Delay Estimation (TDE) and we demonstrate the shortcomings of TDE techniques based on second-order statistics when the noise is of SaS nature. We propose an altemative to second-order based methods, based on Fractional Lower-Order Statistics, and demonstrate the achieved improvement via simulation experiments.