Speech Enhancement Based on ICA and Adaptive Wavelet Thresholding in Stationary and Non Stationary Noise Environment

This paper presents a new approach to speech signal Enhancement in case of white Gaussian noise as well as in highly Non-Stationary Environment. Human ear mostly perceives mixture of various speech sources, but they are intended to interpret desired single speech signal. The proposed system is based on fundamental blind source separation technique known as Independent Component Analysis along with adaptive wavelet Thresholding scheme which enhances signal. An Independent Component Analysis is technique which effectively separates various statistically independent components from input mixture speech signal vectors. An Independent component analysis produces mostly accurate estimates of original speech sources, this basic phenomenon is incorporated to separate out speech signal and noise signal from a mixture of individual sources. Furthermore, Adaptive wavelet domain Thresholding is implied on estimated source signal to improve quality and intelligibility. Threshold value is adaptively estimated for different input signal with noise estimation method. The Time as well as frequency domain Objective Quality Measures such as Log-Likelihood Ratio (LLR), Frequency weighted segmental SNR (fwSNRseg), Weighted Spectral Slope (WSS), Perceptual Evaluation of speech Quality (PESQ), Itakura-Saito (IS) Ratio are then evaluated for resultant Enhanced speech signal with respect to the original desired signal.