Speech Enhancement Based on Approximate Message Passing

作     者：Chao Li Ting Jiang Sheng Wu Chao Li;Ting Jiang;Sheng Wu

作者机构：School of Information and Communication EngineeringBeijing University of Posts and TelecommunicationsBeijing 100876China 

出 版 物：《China Communications》 (中国通信（英文版）)

年 卷 期：2020年第17卷第8期

页      面：187-198页

核心收录：

学科分类：0711[理学-系统科学] 07[理学] 

基　　金：supported by National Natural Science Foundation of China(NSFC)(No.61671075) Major Program of National Natural Science Foundation of China(No.61631003) 

主　　题：speech enhancement approximate message passing Gaussian model expectation maximization algorithm 

摘      要：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).