Noise Cancellation Method for Speech Signal by Using an Extension Type UKF

Abstract

Numerous noise suppression methods for speech signals have been developed up to now. In this paper, a new method to suppress noise in speech signals is proposed by use of an extension type Unscented Kalman filter (UKF). A method considering non-Gaussian noise is proposed theoretically by introducing an expansion expression of Bayes' theorem and considering nonlinear correlation information between the speech signal and the observation data. Specifically, by selecting appropriately the sample points and the weight coefficients, an estimation algorithm of the speech signal for nonliner system is derived on the basis of conditional probability distribution. Moreover, expansion coefficients in the estimation algorithm are realized by considering the higher order correlation information. Improvement for the precise estimation is expected by considering non-Gaussian property. The effectiveness of the proposed method is confirmed by applying it to speech signals contaminated by noises.