Speech Enhancement Based on Approximate Message Passing

Abstract

Abstract: A novel speech improvement technique based on the approximate message passing (AMP) is adopted to get beyond the drawbacks of traditional speech enhancement methods, such as inaccurate voice activity detector (VAD) and noise estimation. To eliminate or muffle the noise from the distorted speech, AMP takes advantage of the difference between speech and noise sparsity. The AMP method is used to effectively rebuild clean speech for speech augmentation. More specifically, the prior probability distribution of the speech sparsity coefficient is represented by a Gaussian model. The expectation maximization (EM) technique provides excellent learning of the hyper-parameters of the prior model. We use the k-nearest neighbor (k-NN) approach to learn the sparsity while taking into account the correlation between the speech coefficients in neighboring frames. Additionally, computational simulations are used to verify the proposed algorithm, which outperforms the three methods which are Kalman filter, principal component analysis (PCA), independent component analysis (ICA) under a variety of signal to noise ratios and compression ratios (SNRs).