Single Channel Adaptive Kalman Filtering – Based Speech Enhancement Algorithm

This paper deals with the problem of speech enhancement when a corrupted speech signal with an additive Gaussian white noise is the only information available for processing. Speech enhancement aims to improve speech quality by using various algorithms. The objective of enhancement is improvement in intelligibility and/or overall perceptual quality of degraded speech signal using audio signal processing techniques. Enhancing of speech degraded by noise, or noise reduction, is the most important field of speech enhancement. Kalman filtering is known as an effective speech enhancement technique, in which speech signal is usually modeled as autoregressive (AR) process and represented in the state-space domain. Kalman filter based approaches proposed in the past, operate in two steps: they first estimate the noise and the driving variances and parameters of the signal model, then estimate the speech signal. Kalman filtering arise some drawbacks. we need to modify the conventional Kalman filter algorithm. Conventional Kalman filter algorithm needs to calculate the parameters of AR(auto=regressive model), and perform a lot of matrix operations, which is generally called as non adaptive. In this paper we provide a alternate solution that avoids explicit estimation of noise and driving process variances by estimating the optimal kalman gain. It eliminates the matrix operations and reduces the computational complexity and we design a coefficient factor for adaptive filtering, to automatically amend the estimation of environmental noise by the observation data. Experimental results shows that the proposed technique is effective for speech enhancement compare to conventional Kalman filter.