Convolutive blind source separation method based on tensor decomposition

Baoze MA; Tianqi ZHANG; Zeliang AN; Pan DENG

doi:10.11959/j.issn.1000-436x.2021140

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Convolutive blind source separation method based on tensor decomposition

Papers | 更新时间：2024-06-05

- Convolutive blind source separation method based on tensor decomposition
- Journal on Communications Vol. 42, Issue 8, Pages: 52-60(2021)
- 作者机构：
  
  重庆邮电大学通信与信息工程学院信号与信息处理重庆市重点实验室，重庆 400065
- 作者简介：
- 基金信息：
  
  The National Natural Science Foundation of China(61671095);The National Natural Science Foundation of China(61371164);The Project of Key Laboratory of Signal and Information Processing of Chongqing(CSTC2009CA2003);The Research Project of Chongqing Educational Commission(KJ130524);The Research Project of Chongqing Educational Commission(KJ1600427);The Research Project of Chongqing Educational Commission(KJ1600429)
- DOI：10.11959/j.issn.1000-436x.2021140
  CLC： TN911.6
- Online First：2021-08，
  
  Published：25 August 2021
- 稿件说明：
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Baoze MA, Tianqi ZHANG, Zeliang AN, et al. Convolutive blind source separation method based on tensor decomposition[J]. Journal on Communications, 2021, 42(8): 52-60.
DOI：

Baoze MA, Tianqi ZHANG, Zeliang AN, et al. Convolutive blind source separation method based on tensor decomposition[J]. Journal on Communications, 2021, 42(8): 52-60. DOI： 10.11959/j.issn.1000-436x.2021140.

摘要

基于张量分解框架提出了一种卷积盲源分离方法，同时解决了混合滤波器矩阵估计和频点排序的问题。首先，根据观测信号的估计自相关矩阵构造出所有频点处的张量模型；然后，利用张量分解技术计算出每个频点上对应的因子矩阵作为该频点的估计混合滤波器矩阵；最后，采用以功率比作为测度的全局优化排序策略消除了全频段的排序模糊性。实验表明，所提方法在不同仿真条件下处理卷积混合的实测语音时表现出了比现有算法更优异的分离性能。

Abstract

A convolutive blind source separation algorithm was proposed based on tensor decomposition framework

to address the estimation of mixed filter matrix and the permutation alignment of frequency bin simultaneously.Firstly

the tensor models at all frequency bins were constructed according to the estimated autocorrelation matrix of the observed signals.Secondly

the factor matrix corresponding to each frequency bin was calculated by tensor decomposition technique as the estimated mixed filter matrix for that bin.Finally

a global optimal permutation strategy with power ratio as the permutation alignment measure was adopted to eliminate the permutation ambiguity in all the frequency bins.Experimental results demonstrate that the proposed method achieves better separation performance than other existing algorithms when dealing with convolutive mixed speech under different simulation conditions.

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references

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