Implementation of Bayesian recursive state-space Kalman filter for noise reduction of speech signal

Noise reduction of speech signals plays an important role in telecommunication systems. Various types of speech additive noise can be introduced such as babble, crowd, large city, and highway which are the main factor of degradation in perceived speech quality. There are some cases on the receiver side of telecommunication systems, where the direct value of interfering noise is not available and there is just access to noisy speech. In these cases the noise cannot be cancelled totally but it may be possible to reduce the noise in a sensible way by utilizing the statistics of the noise and speech signal. In this paper the proposed method for noise reduction is Bayesian recursive state-space Kalman filter, which is a method for estimation of a speech signal from its noisy version. It utilizes the prior probability distributions of the signal and noise processes, which are assumed to be zero-mean Gaussian processes. The function of Kalman filter is assessed for different types of noise such as babble, crowd, large city, and highway. The noise cancellation is implemented for each of aforementioned noises which their powers vary in a range of values. This method of noise reduction yields better speech perceived quality and efficient results compared to Wiener filter.