Robust adaptive Kalman filtering-based speech enhancement algorithm

The 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 the speech signal is usually modeled as an autoregressive (AR) model and represented in the state-space domain. Various approaches based on the Kalman filter have been presented in the literature. They usually operate in two steps: first, additive noise and driving process statistics and speech model parameters are estimated and second, the speech signal is estimated by using Kalman filtering. In the paper, sequential estimators are used for suboptimal adaptive estimation of the unknown a priori driving process and of additive noise statistics simultaneously with the system state. The estimation of time-varying AR signal model is based on a robust recursive least-square algorithm with variable forgetting factor. The proposed algorithm provides improved state estimates at little computational expense.