Speech enhancement based on approximate message passing

Abstract

To overcome the limitations of conventional speech enhancement methods, such as inaccurate voice activity detector (VAD) and noise estimation, a novel speech enhancement algorithm based on the approximate message passing (AMP) is adopted. AMP exploits the difference between speech and noise sparsity to remove or mute the noise from the corrupted speech. The AMP algorithm is adopted to reconstruct the clean speech efficiently for speech enhancement. More specifically, the prior probability distribution of speech sparsity coefficient is characterized by Gaussian-model, and the hyper-parameters of the prior model are excellently learned by expectation maximization (EM) algorithm. We utilize the k-nearest neighbor (k-NN) algorithm to learn the sparsity with the fact that the speech coefficients between adjacent frames are correlated. In addition, computational simulations are used to validate the proposed algorithm, which achieves better speech enhancement performance than other four baseline methods-Wiener filtering, subspace pursuit (SP), distributed sparsity adaptive matching pursuit (DSAMP), and expectation-maximization Gaussian-model approximate message passing (EM-GAMP) under different compression ratios and a wide range of signal to noise ratios (SNRs).