Improved Subspace-Based Single-Channel Speech Enhancement Using Generalized Super-Gaussian Priors

Traditional single-channel subspace-based schemes for speech enhancement rely mostly on linear minimum mean-square error estimators, which are globally optimal only if the Karhunen-Loeacuteve transform (KLT) coefficients of the noise and speech processes are Gaussian distributed. We derive in this paper subspace-based nonlinear estimators assuming that the speech KLT coefficients are distributed according to a generalized super-Gaussian distribution which has as special cases the Laplacian and the two-sided Gamma distribution. As with the traditional linear estimators, the derived estimators are functions of the a priori signal-to-noise ratio (SNR) in the subspaces spanned by the KLT transform vectors. We propose a scheme for estimating these a priori SNRs, which is in fact a generalization of the "decision-directed" approach which is well-known from short-time Fourier transform (STFT)-based enhancement schemes. We show that the proposed a priori SNR estimation scheme leads to a significant reduction of the residual noise level, a conclusion which is confirmed in extensive objective speech quality evaluations as well as subjective tests. We also show that the derived estimators based on the super-Gaussian KLT coefficient distribution lead to improvements for different noise sources and levels as compared to when a Gaussian assumption is imposed