A denoising method for acoustic signal based on kernel independent component analysis

YANG Xu; ZHANG Xue-yuan; LI Bao-qing

doi:10.13873/j.1000-97872011.11.028

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A denoising method for acoustic signal based on kernel independent component analysis

更新时间：2025-09-11

- A denoising method for acoustic signal based on kernel independent component analysis
- Vol. 30, Issue 11, Pages: 43-45+48(2011)
- 作者机构：
  
  中国科学院上海微系统与信息技术研究所无线传感网络实验室
- 作者简介：
- 基金信息：
- DOI：10.13873/j.1000-97872011.11.028
  CLC： TN911.4
- Published：2011
- 稿件说明：
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[1]杨旭,张学渊,李宝清.基于核独立成分分析的声信号去噪方法[J].传感器与微系统,2011,30(11):43-45+48.

YANG Xu, ZHANG Xue-yuan, LI Bao-qing. A denoising method for acoustic signal based on kernel independent component analysis[J]. 2011, 30(11): 43-45+48.
[1]杨旭,张学渊,李宝清.基于核独立成分分析的声信号去噪方法[J].传感器与微系统,2011,30(11):43-45+48. DOI： 10.13873/j.1000-97872011.11.028.

YANG Xu, ZHANG Xue-yuan, LI Bao-qing. A denoising method for acoustic signal based on kernel independent component analysis[J]. 2011, 30(11): 43-45+48. DOI： 10.13873/j.1000-97872011.11.028.

摘要

野外环境无线传感侦查网络中的声识别技术面临着复杂的自然环境噪声的挑战

尤其是由强风噪声造成的影响。独立成分分析（ICA）方法是一种能够较好地解决这种复杂环境去噪的方法。引入一种基于核方法的非线性ICA方法——核独立成分分析（KICA）。基于该算法

针对强风噪声的特性

设计一种应用于单声传感器降噪的方案。通过降噪仿真实验

对KICA与ICA的典型算法快速ICA算法进行比较。实验结果表明:在以均方误差作为指标的降噪性能和实际分类识别率两方面

KICA算法相较于快速ICA算法对于该种强噪声具有更为优秀的降噪效果。

Abstract

The acoustic recognition technology in wireless sensor surveillance network in wild environment is facing the challenge of the complicated and strong acoustic noise

especially the effect of the wind noise.Independent component analysis（ICA）method has a good performance in denoising in complicated environment.On the basis of kernel independent component analysis（KICA）

a denoising scheme for single acoustic sensor is designed.It is a typical nonlinear independent component analysis method.Through denoising simulation experiment

the KICA algorithm is compared with the typical algorithm of ICA method

fast ICA algorithm.Results show that in a denoising application aimed at the strong wind noise in wild environment

with the mean square error criterion and the correct classification ratio criterion

KICA algorithm is superior to the fast ICA algorithm.

关键词

Keywords

references

RESEARCH ON THEORETIC EVIDENCE AND REALIZATION OF DIRECTLY-MEAN EMD METHOD [J]. Zhong YoumingQin ShurenTang BaopingDepartment of Mechanical Engineering,Chongqing University,Chongqing 400030, China. Chinese Journal of Mechanical Engineering . 2004(03)

Improved DS acoustic–seismic modality fusion for ground-moving target classification in wireless sensor networks [J] . Qiang Pan,Jianming Wei,Hongbing Cao,Na Li,Haitao Liu. Pattern Recognition Letters . 2007 (16)

Independent component analysis: algorithms and applications [J] . Neural Networks . 2000 (4)

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