«上一篇/Previous Article|本期目录/Table of Contents|下一篇/Next Article»

《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:

27

期数:

2017年06期

页码:

160-164

栏目:

应用开发研究

出版日期:

2017-06-10

文章信息/Info

Title:

 Application of Smoothed L0 Algorithm in Compressed Sensing Reconstruction of Speech Signal

文章编号:

1673-629X（2017)06-0160-05

作者:

 薛海双; 孙林慧; 欧国振

 南京邮电大学 通信与信息工程学院

Author(s):

 XUE Hai-shuang; SUN Lin-hui ; OU Guo-zhen

关键词:

 ; 压缩感知; 语音重构; 重构算法; 平滑L0算法; 平滑函数; L0范数

Keywords:

 ; compressed sensing; speech reconstruction; algorithms of reconstruction; smoothed L0 algorithm; smooth function; L0 norm

分类号:

TP39

文献标志码:

A

摘要:

 语音信号在频域和离散余弦变换域等都具有良好的稀疏特性,满足压缩感知的先验条件,因此可以基于压缩感知对语音信号进行处理.语音压缩感知主要包括三个方面:稀疏基、观测矩阵和重构算法.其中,重构算法直接影响着重构信号的质量,是最重要的一部分.传统的语音压缩感知常基于正交匹配追踪算法进行重构.正交匹配追踪算法要求已知信号稀疏度,增加了实现的难度.为了提高语音信号的重构质量、简化实现过程,提出了一种基于平滑L0算法的语音压缩重构模型.平滑L0算法是用平滑函数逼近L0范数,它不需要提前知道信号的稀疏度,具有计算量低、重构质量高等优点.此外,提出了一种新的平滑函数,并基于高斯函数和新的平滑函数来验证平滑L0算法在语音压缩重构中的优越性.实验结果表明,在相同的条件下,相比于正交匹配追踪算法,使用平滑L0算法对语音进行重构,不仅缩短了重构时间,而且大大提高了重构质量.

Abstract:

 At present,speech signals have good sparsities in domains like frequency and Discrete Cosine Transformation (DCT) and so on,which satisfies the prerequisite for Compressed Sensing (CS).Therefore,it can be treated by CS theory,which consists of sparse representation of the signal,design of the measurement matrix and the algorithms of reconstruction.Among them,the most important part is reconstruction algorithms which can influence the quality of reconstructed signals directly.The traditional compressed sensing reconstruction of speech is usually based on Orthogonal Matching Pursuit (OMP) method.The orthogonal matching pursuit method needs to obtain sparse priors of the speech signal in advance,which makes the realization difficult.In order to improve the reconstruction quality of speech signal and simplify the implementation process,a compressed speech’’s reconstruction method based on Smoothed L0 (SL0) algorithm has been proposed,in which the SL0 uses smooth function to approximate L0 norm without acquisition of sparse priors of the speech signal in advance and with advantages of lower calculation capacity and higher quality of reconstruction.In addition,a new smooth function has been proposed.Gaussian function and the new smooth function are used to confirm the performance of the SL0.Simulation results demonstrate that the SL0 algorithm has not only obtained a higher quality of reconstruction than the traditional OMP method,but also shorten the implementation time.

常用功能

更新日期/Last Update: 2017-07-28