An Adaptive Kalman Filter for the Enhancement of Speech Signals

This paper deals with the problem of speech enhancement when only a corrupted speech signal is available for processing. Kalman filtering is known as an effective speech enhancement technique, in which speech signal is usually modeled as autoregressive (AR) model and represented in the state-space domain. Various approaches based on the Kalman filter are presented in the literature. They usually operate in two steps: first, additive noise and driving process variances and speech model parameters are estimated and second, the speech signal is estimated by using Kalman filtering. In this paper sequential estimators are used for sub-optimal adaptive estimation of the unknown a priori driving process and additive noise statistics simultaneously with the system state. The estimation of time-varying AR signal model is based on weighted recursive least square algorithm with variable forgetting factor. The proposed algorithm provides improved state estimates at little computational expense.