Nonlinear innovation to noisy blind source separation based on gaussian moments

Hongjuan Zhang; Chonghui Guo; Zhenwei Shi; Enmin Feng

doi:10.1007/978-3-540-85984-0_85

Nonlinear innovation to noisy blind source separation based on gaussian moments

Hongjuan Zhang^*
, Chonghui Guo
, Zhenwei Shi
, Enmin Feng

^*Corresponding author for this work

School of Astronautics

Dalian University of Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

This paper addresses blind source separation problem for noisy data based on the concepts of nonlinear innovation and Gaussian moments. An objective function which incorporates Gaussian moments and the nonlinear innovation of original sources is developed. Minimizing this objective function, a noisy blind source separation algorithm is proposed when the noise covariance is known and source signals are nonstationary in the sense that the variance of each is assumed to change smoothly as a function of time. In addition, this method is further extended to the case of noise covariance unknown. Validity and performance of the described approaches are demonstrated by computer simulations.

Original language	English
Title of host publication	Advanced Intelligent Computing Theories and Applications
Subtitle of host publication	With Aspects of Artificial Intelligence - 4th International Conference on Intelligent Computing, ICIC 2008, Proceedings
Pages	709-716
Number of pages	8
DOIs	https://doi.org/10.1007/978-3-540-85984-0_85
State	Published - 2008
Event	4th International Conference on Intelligent Computing, ICIC 2008 - Shanghai, China Duration: 15 Sep 2008 → 18 Sep 2008

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	5227 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	4th International Conference on Intelligent Computing, ICIC 2008
Country/Territory	China
City	Shanghai
Period	15/09/08 → 18/09/08

Access to Document

10.1007/978-3-540-85984-0_85

Cite this

Zhang, H., Guo, C., Shi, Z., & Feng, E. (2008). Nonlinear innovation to noisy blind source separation based on gaussian moments. In Advanced Intelligent Computing Theories and Applications: With Aspects of Artificial Intelligence - 4th International Conference on Intelligent Computing, ICIC 2008, Proceedings (pp. 709-716). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5227 LNAI). https://doi.org/10.1007/978-3-540-85984-0_85

Zhang, Hongjuan ; Guo, Chonghui ; Shi, Zhenwei et al. / Nonlinear innovation to noisy blind source separation based on gaussian moments. Advanced Intelligent Computing Theories and Applications: With Aspects of Artificial Intelligence - 4th International Conference on Intelligent Computing, ICIC 2008, Proceedings. 2008. pp. 709-716 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{b9178373744344e5bdf2060a2039caae,

title = "Nonlinear innovation to noisy blind source separation based on gaussian moments",

abstract = "This paper addresses blind source separation problem for noisy data based on the concepts of nonlinear innovation and Gaussian moments. An objective function which incorporates Gaussian moments and the nonlinear innovation of original sources is developed. Minimizing this objective function, a noisy blind source separation algorithm is proposed when the noise covariance is known and source signals are nonstationary in the sense that the variance of each is assumed to change smoothly as a function of time. In addition, this method is further extended to the case of noise covariance unknown. Validity and performance of the described approaches are demonstrated by computer simulations.",

author = "Hongjuan Zhang and Chonghui Guo and Zhenwei Shi and Enmin Feng",

year = "2008",

doi = "10.1007/978-3-540-85984-0\_85",

language = "英语",

isbn = "3540859837",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

pages = "709--716",

booktitle = "Advanced Intelligent Computing Theories and Applications",

note = "4th International Conference on Intelligent Computing, ICIC 2008 ; Conference date: 15-09-2008 Through 18-09-2008",

}

Zhang, H, Guo, C, Shi, Z & Feng, E 2008, Nonlinear innovation to noisy blind source separation based on gaussian moments. in Advanced Intelligent Computing Theories and Applications: With Aspects of Artificial Intelligence - 4th International Conference on Intelligent Computing, ICIC 2008, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 5227 LNAI, pp. 709-716, 4th International Conference on Intelligent Computing, ICIC 2008, Shanghai, China, 15/09/08. https://doi.org/10.1007/978-3-540-85984-0_85

Nonlinear innovation to noisy blind source separation based on gaussian moments. / Zhang, Hongjuan; Guo, Chonghui; Shi, Zhenwei et al.
Advanced Intelligent Computing Theories and Applications: With Aspects of Artificial Intelligence - 4th International Conference on Intelligent Computing, ICIC 2008, Proceedings. 2008. p. 709-716 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5227 LNAI).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Shi, Zhenwei

AU - Feng, Enmin

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N2 - This paper addresses blind source separation problem for noisy data based on the concepts of nonlinear innovation and Gaussian moments. An objective function which incorporates Gaussian moments and the nonlinear innovation of original sources is developed. Minimizing this objective function, a noisy blind source separation algorithm is proposed when the noise covariance is known and source signals are nonstationary in the sense that the variance of each is assumed to change smoothly as a function of time. In addition, this method is further extended to the case of noise covariance unknown. Validity and performance of the described approaches are demonstrated by computer simulations.

AB - This paper addresses blind source separation problem for noisy data based on the concepts of nonlinear innovation and Gaussian moments. An objective function which incorporates Gaussian moments and the nonlinear innovation of original sources is developed. Minimizing this objective function, a noisy blind source separation algorithm is proposed when the noise covariance is known and source signals are nonstationary in the sense that the variance of each is assumed to change smoothly as a function of time. In addition, this method is further extended to the case of noise covariance unknown. Validity and performance of the described approaches are demonstrated by computer simulations.

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DO - 10.1007/978-3-540-85984-0_85

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SN - 9783540859833

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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T2 - 4th International Conference on Intelligent Computing, ICIC 2008

Y2 - 15 September 2008 through 18 September 2008

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Zhang H, Guo C, Shi Z, Feng E. Nonlinear innovation to noisy blind source separation based on gaussian moments. In Advanced Intelligent Computing Theories and Applications: With Aspects of Artificial Intelligence - 4th International Conference on Intelligent Computing, ICIC 2008, Proceedings. 2008. p. 709-716. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-540-85984-0_85

Nonlinear innovation to noisy blind source separation based on gaussian moments

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