Speech enhancement using a modified Kalman filter based on complex linear prediction and supergaussian priors

This paper presents a modified Kalman filter operating in the frequency domain for single channel speech enhancement. The proposed scheme uses a two step approach. In the first step, information from previous, enhanced speech DFT coefficients is exploited to perform an estimation of the current speech coefficients. Investigations show that the highest prediction gain is achieved by modeling the temporal trajectory of the speech DFT coefficients as a complex autoregressive (AR) process. In the second step, the first prediction is updated using three alternative spectral estimators, including the conventional Kalman filter gain. Instrumental measurements show the improvement of the proposed scheme compared to purely statistical weighting rules.