Adaptive suppression of non-stationary noise by using the variational Bayesian method

This paper proposes an adaptive noise suppression method for non-stationary noise based on the Bayesian estimation method. The following conditions are assumed: (1) Speech and noise samples are statistically independent, and they follow auto-regressive (AR) processes. (2) The prior distribution of the parameters of the noise AR model of a current frame is identical to the posterior distribution of those parameters calculated in the previous frame. Under these conditions, the proposed method approximates the joint posterior distribution of the AR model parameters and the speech samples by using the variational Bayesian method. Furthermore, we describe an efficient implementation by assuming that all involved covariance matrices have the Toeplitz structure. The proposed method was tested on real speech and noise signals and compared with other noise suppression methods.