Compressive speech enhancement using semi-soft thresholding and improved threshold estimation

Abstract

Compressive speech enhancement is based on the compressive sensing (CS) sampling theory and utilizes the sparsity of the signal for its enhancement. To improve the performance of the discrete wavelet transform (DWT) basis-function based compressive speech enhancement algorithm, this study presents a semi-soft thresholding approach suggesting improved threshold estimation and threshold rescaling parameters. The semi-soft thresholding approach utilizes two thresholds, one threshold value is an improved universal threshold and the other is calculated based on the initial-silence-region of the signal. This study suggests that thresholding should be applied to both detail coefficients and approximation coefficients to remove noise effectively. The performances of the hard, soft, garrote and semi-soft thresholding approaches are compared based on objective quality and speech intelligibility measures. The normalized covariance measure is introduced as an effective intelligibility measure as it has a strong correlation with the intelligibility of the speech signal. A visual inspection of the output signal is used to verify the results. Experiments were conducted on the noisy speech corpus (NOIZEUS) speech database. The experimental results indicate that the proposed method of semi-soft thresholding using improved threshold estimation provides better enhancement compared to the other thresholding approaches.