Nonlinear blind source separation algorithm based on spline interpolation and artificial bee colony optimization

Lei CHEN; Shi-zhong GAN; Li-yi ZHANG; Guang-yan WANG

doi:10.11959/j.issn.1000-436x.2017147

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Nonlinear blind source separation algorithm based on spline interpolation and artificial bee colony optimization

Papers | 更新时间：2024-06-05

- Nonlinear blind source separation algorithm based on spline interpolation and artificial bee colony optimization
- Journal on Communications Vol. 38, Issue 7, Pages: 36-46(2017)
- 作者机构：
  
  1. 天津商业大学信息工程学院，天津 300134
  2. 天津工业大学电子信息工程学院，天津 300387
- 作者简介：
- 基金信息：
  
  The National Natural Science Foundation of China(61401307);The China Postdoctoral Science Foundation(2014M561184);Tianjin Research Program of Application Foundation and Advanced Technology(15JCYBJC17100);Tianjin Research Program of Application Foundation and Advanced Technology of China(14JCZDJC32600);Tianjin Research Program of Science and Technology Commissioner of China(16JCTPJC48400)
- DOI：10.11959/j.issn.1000-436x.2017147
  CLC： TN911.7
- Online First：2017-07，
  
  Published：25 July 2017
- 稿件说明：
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Lei CHEN, Shi-zhong GAN, Li-yi ZHANG, et al. Nonlinear blind source separation algorithm based on spline interpolation and artificial bee colony optimization[J]. Journal on Communications, 2017, 38(7): 36-46.
DOI：

Lei CHEN, Shi-zhong GAN, Li-yi ZHANG, et al. Nonlinear blind source separation algorithm based on spline interpolation and artificial bee colony optimization[J]. Journal on Communications, 2017, 38(7): 36-46. DOI： 10.11959/j.issn.1000-436x.2017147.

摘要

针对更加复杂的非线性混合情况，提出一种基于样条插值拟合与群智能优化的后非线性盲源分离算法。采用样条插值函数拟合去非线性函数，使用负熵作为分离的评价准则，建立分离模型。分离过程采用改进的人工蜂群算法优化求解样条插值节点参数，并在分离的目标函数中引入相关性约束条件进行解空间范围限制，克服分离过程中存在的异常值现象。针对语音数据的分离实验结果表明，所提算法能够有效实现非线性混合信号的盲分离，较传统的基于奇数多项式拟合的分离算法具有更高的分离精度。

Abstract

A post-nonlinear blind source separation algorithm based on spline interpolation fitting and artificial bee colony optimization was proposed for the more complicated nonlinear mixture situations.The separation model was constructed by using the spline interpolation to fit the inverse nonlinear distortion function and using entropy as the separation criterion.The spline interpolation node parameters were solved by the modified artificial bee colony optimization algorithm.The correlation constraint was added into the objective function for limiting the solution space and the outliers wuld be restricted in the separation process.The results of speech sounds separation experiment show that the proposed algorithm can effectively realize the signal separation for the nonlinear mixture.Compared with the traditional separation algorithm based on odd polynomial fitting

the proposed algorithm has higher separation accuracy.

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references

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