Speech enhancement using a Kalman-based normalized LMS algorithm

This paper deals with the problem of Adaptive Noise Cancellation (ANC) for the speech signal corrupted with an additive white Gaussian noise. After explaining the least Mean Square (LMS)-based adaptive filter and Kalman filter, we examine the hybrid Kalman-based LMS (KLMS) technique for adaptation of the ANC. The proposed technique suggests a way to normalize LMS algorithm using Kalman filter. Our simulation shows that the KLMS method converges faster and is more stable compared to the LMS and its Normalized version, NLMS.