Efficient hard and soft thresholding for wavelet speech enhancement

This paper presents an efficient thresholding technique for wavelet speech enhancement. The signal-bias compensated noise level is used as the threshold parameter. The noise as well as signal level is estimated from the detail wavelet packet (WP) coefficients in the first scale. Both hard and soft thresholding are applied successively. The regions for hard thresholding are identified by estimating their signal to noise ratio (SNR) in the wavelet domain. Soft thresholding is applied to the rest of the regions. The performance of the proposed scheme is evaluated on speech recorded in real conditions with artificial noise added to it.