A DISTRIBUTED COMPRESSED SENSING APPROACH FOR SPEECH SIGNAL DENOISING  

作　　者：Ji Yunyun Yang Zhen 

机构地区：[1]College of Communication and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210003, China [2]Key Lab of Broadband Wireless Communication and Sensor Network Technology, Ministry of Education, Nanjing University of Posts ＆ Telecommunications, Nanjing 210003, China

出　　处：《Journal of Electronics(China)》2011年第4期509-517,共9页电子科学学刊（英文版）

基　　金：Supported by the National Natural Science Foundation of China (No. 60971129);the National Research Program of China (973 Program) (No. 2011CB302303);the Scientific Innovation Research Program of College Graduate in Jiangsu Province (No. CXLX11_0408)

摘　　要：Compressed sensing,a new area of signal processing rising in recent years,seeks to minimize the number of samples that is necessary to be taken from a signal for precise reconstruction.The precondition of compressed sensing theory is the sparsity of signals.In this paper,two methods to estimate the sparsity level of the signal are formulated.And then an approach to estimate the sparsity level directly from the noisy signal is presented.Moreover,a scheme based on distributed compressed sensing for speech signal denoising is described in this work which exploits multiple measurements of the noisy speech signal to construct the block-sparse data and then reconstruct the original speech signal using block-sparse model-based Compressive Sampling Matching Pursuit(CoSaMP) algorithm.Several simulation results demonstrate the accuracy of the estimated sparsity level and that this de-noising system for noisy speech signals can achieve favorable performance especially when speech signals suffer severe noise.Compressed sensing,a new area of signal processing rising in recent years,seeks to minimize the number of samples that is necessary to be taken from a signal for precise reconstruction.The precondition of compressed sensing theory is the sparsity of signals.In this paper,two methods to estimate the sparsity level of the signal are formulated.And then an approach to estimate the sparsity level directly from the noisy signal is presented.Moreover,a scheme based on distributed compressed sensing for speech signal denoising is described in this work which exploits multiple measurements of the noisy speech signal to construct the block-sparse data and then reconstruct the original speech signal using block-sparse model-based Compressive Sampling Matching Pursuit（CoSaMP） algorithm.Several simulation results demonstrate the accuracy of the estimated sparsity level and that this de-noising system for noisy speech signals can achieve favorable performance especially when speech signals suffer severe noise.

关 键 词：Distributed compressed sensing Sparsity estimation Speech signal DENOISING 

分 类 号：TN912.3[电子电信—通信与信息系统]