Compressed Sensing Speech Enhancement Algorithm Based on Chaotic Sequences

SUI Hao; ZHOU Ping; SHEN Hao; YANG Qing

Chinese

您当前的位置：

首页 >

文章列表页 >

Compressed Sensing Speech Enhancement Algorithm Based on Chaotic Sequences

更新时间：2026-09-04

- Compressed Sensing Speech Enhancement Algorithm Based on Chaotic Sequences
- Vol. 35, Issue 1, Pages: 96-99(2018)
- 作者机构：
  
  桂林电子科技大学机构电子工程与自动化学院,广西,桂林,541004
- 作者简介：
- 基金信息：
- DOI：
  CLC：
- Published：2018
- 稿件说明：
移动端阅览
隋昊, 周萍, 沈昊, 杨青. 基于混沌序列的压缩感知语音增强算法[J]. 微电子学与计算机, 2018, 35(1): 96-99, 105.

SUI Hao, ZHOU Ping, SHEN Hao, et al. Compressed Sensing Speech Enhancement Algorithm Based on Chaotic Sequences[J]. 2018, 35(1): 96-99.
隋昊, 周萍, 沈昊, 杨青. 基于混沌序列的压缩感知语音增强算法[J]. 微电子学与计算机, 2018, 35(1): 96-99, 105. DOI：

SUI Hao, ZHOU Ping, SHEN Hao, et al. Compressed Sensing Speech Enhancement Algorithm Based on Chaotic Sequences[J]. 2018, 35(1): 96-99. DOI：

摘要

利用语音在离散余弦变换域下的稀疏性

提出一种将混沌序列和符号函数相结合的观测矩阵

该观测矩阵具有良好的伪随机性

实现了确定性和随机性的统一.首先将带噪的语音信号在DCT域进行稀疏

然后利用提出的观测矩阵进行观测

以保留大部分语音特性

最后在原有重构算法基础上改进重构算法

以加快重构速度.此算法可以使重构语音信号的可懂度和清晰度得到大幅提升

实现了语音增强.

Abstract

In this paper

present a kind of observation matrix which combines the chaotic sequence with the sign function

which has good pseudo randomness.Firstly

the noisy speech signal is sparse in the DCT domain

then the observation matrix is used to preserve most of the speech features

finally based on the original reconstruction algorithm

this paper improves the reconstruction algorithm to speed up the reconstruction.The algorithm proposed in this paper makes the intelligibility and clarity of reconstructed speech signal be greatly improved

and the speech enhancement is realized.

关键词

Keywords

references

Blumensath T, Davies M E. Gradient pursuits[J]. IEEE Transaction on Signal Processing, 2008, 56(6): 2370-2382. DOI: 2370

Donoho D L. Compressed sensing[J]. IEEE Transactions on Information Theory, 2006, 52(4): 1289-1306. DOI: 1289

Needell D, Vershynin R. Greedy signal recovery and un-certainty principles[C]//Proceedings of the Conference on Computational Imaging. San Jose, USA, SPIE, 2008: 1-12.

Tropp J, Gilbert A. Signal recovery from random measurements via orthogonal matching pursuit[J]. IEEE Transactions on Information Theory, 2008, 53(12): 4655-4666. DOI: 4655

Candes E J, Tao T. Near-optimal signal recovery from random projections: universal encoding strategies[J]. IEEE Transactions on Information Theory, 2006, 52(12): 5406-5425. DOI: 5406

Do T T, Lu Gan, Nguyen N, et al. Sparsity adaptive matching pursuit algorithm for practical compressed sensing[C]∥Asilomar Conference on Signals, Systems and Computers. Pacific Grove, California: IEEE Press, 2008: 581-587.

石光明, 刘丹华, 高大化, 等. 压缩感知理论及其研究进展[J]. 电子学报, 2009, 37(5): 1070-1081. DOI: 1070

金坚, 谷源涛, 梅顺良. 压缩采样技术及其应用[J]. 电子与信息学报, 2010, 32(2): 470-475. DOI: 470

毛静, 尹鹏亮, 马琦, 等. 自适应压缩感知的语音压缩重构算法研究[J]. 电子技术应用, 2015, 41(2): 72-74. DOI: 72

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Aerial small object detection algorithm based on improved RT-DETR

Simulation on the DC characteristics of double heterojunction p-GaN HEMT with AlN/GaN hybrid buffer layer

Monte carlo analysis of V-layout trench SiC neutron detector

A high-efficiency charge pump for ultra-low voltage boost systems

High gain stability high temperature operational amplifier based on reverse body bias

Related Author

No data

Related Institution

No data

AI问答

Postal code：100079
Tel：（010）53879206 Email：tmw@bjxintong.com.cn
Technical support is provided by Beijing Founder electronics co., LTD 京ICP备09082226号-64 京公网安备11010602201714号
It is recommended to read the content of this site in Chrome&IE9+. Please switch to extreme mode in browser 360.
Cookies We use cookies to help provide and enhance our service and tailor content. By continuing, you agree to the use of cookies.

⁰