Blind-source separation based on decorrelation and nonstationarity

被引：13
|
作者：
Yin, Fuliang [1 ]
Mei, Tiemin
Wang, Jun
机构：
[1] Dalian Univ Technol, Sch Elect & Informat Engn, Dalian 116023, Peoples R China
[2] Shenyang Inst Technol, Sch Informat Sci & Engn, Shenyang 110168, Peoples R China
[3] Chinese Univ Hong Kong, Dept Mech & Automat Engn, Shatin, Hong Kong, Peoples R China
来源：
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-REGULAR PAPERS | 2007年 / 54卷 / 05期
基金：
中国国家自然科学基金;
关键词：
blind-source separation (BSS); decorrelation; natural gradient; nonstationary processes; second-order statistics (SOS); stationary processes;
D O I：
10.1109/TCSI.2007.895510
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
In this paper, discrete-time blind-source separation (BSS) of instantaneous mixtures is studied. Decorrelation-based sufficient criteria for BSS of stationary and nonstionary sources are derived based on nonstationarity and nonwhiteness. A gradient algorithm is proposed based on these criteria. A batch-data algorithm and an on-line algorithm are developed based on the corollaries of the BSS criteria. These algorithms are especially useful for the separation of nonstationary sources. They are robust to additive white noises if the time-delayed decorrelation and the nonstationarity of the sources are considered simultaneously in the algorithms. Experiment results show the effectiveness and performance of the proposed algorithms.
引用
收藏
页码：1150 / 1158
页数：9
相关论文
共 50 条
  • [21] A comparison of decorrelation criteria for the blind source separation of nonstationary signals
    Fancourt, C
    Parra, L
    SAM2002: IEEE SENSOR ARRAY AND MULTICHANNEL SIGNAL PROCESSING WORKSHOP PROCEEDINGS, 2002, : 418 - 421
  • [22] The potential of decorrelation in blind separation of sources based on cumulants
    Zarzoso, V
    Nandi, AK
    SIGNAL ANALYSIS & PREDICTION I, 1997, : 293 - 296
  • [23] A NEW BAYESIAN SOURCE SEPARATION APPROACH TO BLIND DECORRELATION OF SAR DATA
    Wong, Alexander
    Fieguth, Paul
    2010 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM, 2010, : 4035 - 4038
  • [24] Multivariate Slow Feature Analysis and Decorrelation Filtering for Blind Source Separation
    Ha Quang Minh
    Wiskott, Laurenz
    IEEE TRANSACTIONS ON IMAGE PROCESSING, 2013, 22 (07) : 2737 - 2750
  • [25] A unifying criterion for blind source separation and decorrelation: Simultaneous diagonalization of correlation matrices
    Wu, HC
    Principe, JC
    NEURAL NETWORKS FOR SIGNAL PROCESSING VII, 1997, : 496 - 505
  • [26] Convolutive Blind Speech Separation by Decorrelation
    Wang, Fuxiang
    Zhang, Jun
    ADVANCES IN NEURO-INFORMATION PROCESSING, PT I, 2009, 5506 : 737 - 744
  • [27] A frequency domain blind signal separation method based on decorrelation
    Schobben, DWE
    Sommen, PCW
    IEEE TRANSACTIONS ON SIGNAL PROCESSING, 2002, 50 (08) : 1855 - 1865
  • [28] Blind separation of convolutive mixtures by decorrelation
    Mei, TM
    Yin, FL
    SIGNAL PROCESSING, 2004, 84 (12) : 2297 - 2313
  • [29] Noise Source Separation based on the Blind Source Separation
    Yang, Yang
    Li, Zuoli
    Wang, Xiuqin
    Zhang, Di
    2011 CHINESE CONTROL AND DECISION CONFERENCE, VOLS 1-6, 2011, : 2236 - +
  • [30] Blind separation of nonstationary sources by spectral decorrelation
    Hosseini, S
    Deville, Y
    INDEPENDENT COMPONENT ANALYSIS AND BLIND SIGNAL SEPARATION, 2004, 3195 : 279 - 286
12345