基于二阶统计和时间结构的盲信号分离方法

doi:10.11772/j.issn.1001-9081.2014.09.2510

计算机应用 ›› 2014, Vol. 34 ›› Issue (9): 2510-2513.DOI: 10.11772/j.issn.1001-9081.2014.09.2510

基于二阶统计和时间结构的盲信号分离方法

邱萌萌¹,²,周力¹,²,汪磊¹,²,吴建强¹,²

1. 安徽工程大学电气工程学院，安徽芜湖241000;
2. 安徽省电气传动与控制重点实验室(安徽工程大学)，安徽芜湖241000

收稿日期:2014-03-31 修回日期:2014-06-18 出版日期:2014-09-01 发布日期:2014-09-30
通讯作者: 邱萌萌
作者简介:
邱萌萌(1987-), 女,安徽宿州人,硕士研究生,主要研究方向:自动控制、人工智能控制;
周力(1957-),男,安徽芜湖人,教授,主要研究方向:自动控制、人工智能控制;
汪磊(1989-),男,安徽铜陵人,硕士研究生,主要研究方向:自动控制、人工智能控制;
吴建强(1990-),男,安徽黄山人,硕士研究生,主要研究方向:自动控制、人工智能控制。
基金资助:
国家自然科学基金资助项目;安徽省教育厅自然科学基金资助项目;安徽省教育厅自然科学基金重点科研项目;芜湖市科技局的科研项目

Blind separation method for source signals with temporal structure based on second-order statistics

QIU Mengmeng¹,²,ZHOU Li¹,²,WANG Lei¹,²,WU Jianqiang¹,²

1. Anhui Key Laboratory of Electric Drive and Control (Anhui Polytechnic University), Wuhu Anhui 241000, China
2. College of Electrical Engineering, Anhui Polytechnic University, Wuhu Anhui 241000, China

Received:2014-03-31 Revised:2014-06-18 Online:2014-09-01 Published:2014-09-30
Contact: QIU Mengmeng

摘要/Abstract

摘要：

盲源分离(BSS)的目标就是在混合过程未知的情况下，仅仅依据观测得到的混合信号，恢复出不能直接观测的源信号。针对具有时间结构的源信号，即各个源信号分量满足空间上不相关但时间上相关，提出了一种基于二阶统计量的盲源分离方法。该方法首先对混合信号进行鲁棒预白化处理，其中依据最小描述长度准则对源信号的维数进行估计；然后通过对白化信号的时延协方差矩阵进行奇异值分解(SVD)，从而实现源信号的盲分离。仿真中通过对一组语音信号的分离验证了算法的效果，并利用信号干扰比(SIR)和性能指标函数(PI)两个指标定量地对算法的性能进行了度量。

Abstract:

The objective of Blind Source Separation (BSS) is to restore the unobservable source signals from their mixtures without knowing the prior knowledge of the mixing process. It is considered that the potential source signals are spatially uncorrelated but temporally correlated, i.e. they have non-vanishing temporal structure. A second-order statistics based BSS method was proposed for such sources. The robust prewhitening was firstly performed on the observed mixing signals, where the dimension of the sources was estimated based on the Minimum Description Length (MDL) criterion. Then, the blind separation was realized by implementing the Singular Value Decomposition (SVD) on the time-delayed covariance matrix of the whitened signals. The simulation on separation of a group of speech signals proves the effectiveness of the algorithm, and the performance of the algorithm was measured by Signal-to-Interference Ratio (SIR) and Performance Index (PI).

中图分类号:

TN911.7

邱萌萌周力汪磊吴建强. 基于二阶统计和时间结构的盲信号分离方法[J]. 计算机应用, 2014, 34(9): 2510-2513.

QIU Mengmeng ZHOU Li WANG Lei WU Jianqiang. Blind separation method for source signals with temporal structure based on second-order statistics[J]. Journal of Computer Applications, 2014, 34(9): 2510-2513.

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基于二阶统计和时间结构的盲信号分离方法

Blind separation method for source signals with temporal structure based on second-order statistics

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