Noise Free Speech Enhancement based on Fast Adaptive Kalman Filtering Algorithm

Speech enhancement is the process of eliminating noise and increasing the quality of a speech signal, which is contaminated with other kinds of distortions. Conventional Kalman filtering is known as an effective speech enhancement technique, in which speech signal is usually modeled as autoregressive (AR) model and perform a lot of matrix operations. In this paper we proposed a fast adaptive algorithm in presence of environment noise which eliminates the matrix operations and reduces the calculating time by only constantly updating the first value of state vector X(n). To evaluate the system performance we employed the calculation of SNR. Simulation results show that the fast adaptive algorithm using Kalman filtering is effective for speech enhancement