An adaptive Kalman filter for the enhancement of speech signals in colored noise

This paper deals with the problem of speech enhancement when a corrupted speech signal with an additive colored noise is the only information available for processing. Kalman filtering is known as an effective speech enhancement technique, in which speech signal is usually modeled as autoregressive (AR) process and represented in the state-space domain. In the above context, all the Kalman filter-based approaches proposed in the past, operate in two steps: first, the noise and the signal parameters are estimated, and second, the speech signal is estimated by using Kalman filtering. In this paper a new sequential estimators are developed for sub-optimal adaptive estimation of the unknown a priori driving processes variances simultaneously with the system state. A weighted recursive least-square algorithm with variable forgetting factor is used for the estimation of the speech AR parameters and a recursively least-squares lattice algorithm is used for the estimation of the noise AR parameters. The algorithm provides improved speech estimate at little computational expense.