A dual-linear predictor approach to blind source extraction for noisy mixtures

Wei Liu; Danilo P. Mandic; Andrzej Cichocki

doi:10.1109/SAM.2008.4606924

A dual-linear predictor approach to blind source extraction for noisy mixtures

Wei Liu, Danilo P. Mandic, Andrzej Cichocki

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

5 Citations (Scopus)

Abstract

A second-order statistics based dual-linear predictor structure is proposed for blind source extraction from noisy instantaneous mixtures. The noise component is assumed to be spatially and temporally white, but the variance information of noise is not required. A detailed proof of the proposed approach is provided and an adaptive algorithm is developed. Simulation results show that it can extract the source signals successfully.

Original language	English
Title of host publication	SAM 2008 - 5th IEEE Sensor Array and Multichannel Signal Processing Workshop
Pages	515-519
Number of pages	5
DOIs	https://doi.org/10.1109/SAM.2008.4606924
Publication status	Published - Aug 2008
Event	SAM 2008 - 5th IEEE Sensor Array and Multichannel Signal Processing Workshop - Darmstadt, Germany Duration: 21 Jul 2008 → 23 Jul 2008

Publication series

Name	SAM 2008 - 5th IEEE Sensor Array and Multichannel Signal Processing Workshop

Conference

Conference	SAM 2008 - 5th IEEE Sensor Array and Multichannel Signal Processing Workshop
Country/Territory	Germany
City	Darmstadt
Period	21/07/08 → 23/07/08

Keywords

Blind source extraction
Linear predictor
Noisy mixtures
Second-order statistics

ASJC Scopus subject areas

Signal Processing
Electrical and Electronic Engineering

More information

10.1109/SAM.2008.4606924

Cite this

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title = "A dual-linear predictor approach to blind source extraction for noisy mixtures",

abstract = "A second-order statistics based dual-linear predictor structure is proposed for blind source extraction from noisy instantaneous mixtures. The noise component is assumed to be spatially and temporally white, but the variance information of noise is not required. A detailed proof of the proposed approach is provided and an adaptive algorithm is developed. Simulation results show that it can extract the source signals successfully.",

keywords = "Blind source extraction, Linear predictor, Noisy mixtures, Second-order statistics",

author = "Wei Liu and Mandic, {Danilo P.} and Andrzej Cichocki",

year = "2008",

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doi = "10.1109/SAM.2008.4606924",

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Liu, W, Mandic, DP & Cichocki, A 2008, A dual-linear predictor approach to blind source extraction for noisy mixtures. in SAM 2008 - 5th IEEE Sensor Array and Multichannel Signal Processing Workshop., 4606924, SAM 2008 - 5th IEEE Sensor Array and Multichannel Signal Processing Workshop, pp. 515-519, SAM 2008 - 5th IEEE Sensor Array and Multichannel Signal Processing Workshop, Darmstadt, Germany, 21/07/08. https://doi.org/10.1109/SAM.2008.4606924

A dual-linear predictor approach to blind source extraction for noisy mixtures. / Liu, Wei; Mandic, Danilo P.; Cichocki, Andrzej.
SAM 2008 - 5th IEEE Sensor Array and Multichannel Signal Processing Workshop. 2008. p. 515-519 4606924 (SAM 2008 - 5th IEEE Sensor Array and Multichannel Signal Processing Workshop).

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

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AB - A second-order statistics based dual-linear predictor structure is proposed for blind source extraction from noisy instantaneous mixtures. The noise component is assumed to be spatially and temporally white, but the variance information of noise is not required. A detailed proof of the proposed approach is provided and an adaptive algorithm is developed. Simulation results show that it can extract the source signals successfully.

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T3 - SAM 2008 - 5th IEEE Sensor Array and Multichannel Signal Processing Workshop

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BT - SAM 2008 - 5th IEEE Sensor Array and Multichannel Signal Processing Workshop

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A dual-linear predictor approach to blind source extraction for noisy mixtures

Abstract

Publication series

Conference

Keywords

ASJC Scopus subject areas

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