A Kalman Filter based Fast Noise Suppression Algorithm

We have proposed a robust noise suppression algorithm with Kalman filter theory [7]. In this paper, we propose a fast noise suppression algorithm by modifying the canonical state space model in [7]. The algorithm aims to achieve robust noise suppression with reduced computational complexity without sacrificing high quality of speech signal. The remarkable features of the proposed algorithm are that it can be realized by 3 multiplications and that it has the same performances or better ones compared with [7] despite the reduction of computational complexity under the same environments, using only the Kalman filter algorithm for the proposed canonical state space model with the colored driving source: (i) a vector state equation is composed of the only speech signal, and (ii) a scalar observation equation is composed of speech signal and additive noise. We have confirmation of validity of the proposed canonical state space model with the colored driving source, and also show the effectiveness through numerical results and subjective evaluation results.