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Source extraction by maximizing the variance in the conditional distribution tails

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Abstract
This paper presents a method for signal extraction based on conditional second-order moments of the output of the extraction filter. The estimator of the filter is derived from an approximate maximum likelihood criterion conditioned on a presence indicator of the source of interest. The conditional moment is shown to be a contrast function under the conditions that 1) all cross-moments of the same order between the source signal of interest and the other source signals are null and 2) that the source of interest has the largest conditional moment among all sources. For the two-source two-observation case, this allows us to derive the theoretical recovery bounds of the contrast when the conditional cross-moment does not vanish. A comparison with empirical results confirms these bounds. Simulations show that the estimator is quite robust to additive Gaussian distributed noise. Also through simulations, we show that the error level induced by a rough approximation of the presence indicator shows a strong similarity with that of additive noise. The robustness, with respect both to noise and to inaccuracies in the prior information about the source presence, guarantees a wide applicability of the proposed method.
Keywords
constrast functions, estimation, Signal Processing, Conditional likelihood, source extraction, INDEPENDENT COMPONENT ANALYSIS, BLIND SIGNAL EXTRACTION, SOURCE SEPARATION, MAXIMUM-LIKELIHOOD, CONSTRAINED ICA, DICTIONARIES, ALGORITHMS, CRITERIA, MIXTURE

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Citation

Please use this url to cite or link to this publication:

MLA
Phlypo, Ronald, Vicente Zarzoso, and Ignace Lemahieu. “Source Extraction by Maximizing the Variance in the Conditional Distribution Tails.” IEEE TRANSACTIONS ON SIGNAL PROCESSING 58.1 (2010): 305–316. Print.
APA
Phlypo, R., Zarzoso, V., & Lemahieu, I. (2010). Source extraction by maximizing the variance in the conditional distribution tails. IEEE TRANSACTIONS ON SIGNAL PROCESSING, 58(1), 305–316.
Chicago author-date
Phlypo, Ronald, Vicente Zarzoso, and Ignace Lemahieu. 2010. “Source Extraction by Maximizing the Variance in the Conditional Distribution Tails.” Ieee Transactions on Signal Processing 58 (1): 305–316.
Chicago author-date (all authors)
Phlypo, Ronald, Vicente Zarzoso, and Ignace Lemahieu. 2010. “Source Extraction by Maximizing the Variance in the Conditional Distribution Tails.” Ieee Transactions on Signal Processing 58 (1): 305–316.
Vancouver
1.
Phlypo R, Zarzoso V, Lemahieu I. Source extraction by maximizing the variance in the conditional distribution tails. IEEE TRANSACTIONS ON SIGNAL PROCESSING. 2010;58(1):305–16.
IEEE
[1]
R. Phlypo, V. Zarzoso, and I. Lemahieu, “Source extraction by maximizing the variance in the conditional distribution tails,” IEEE TRANSACTIONS ON SIGNAL PROCESSING, vol. 58, no. 1, pp. 305–316, 2010.
@article{815521,
  abstract     = {This paper presents a method for signal extraction
based on conditional second-order moments of the output of the extraction filter. The estimator of the filter is derived from an approximate maximum likelihood criterion conditioned on a presence indicator of the source of interest. The conditional moment is shown to be a contrast function under the conditions that 1) all cross-moments of the same order between the source signal of interest and the other source signals are null and 2) that the source of interest has the largest conditional moment among all sources. For the two-source two-observation case, this allows us to derive the theoretical recovery bounds of the contrast when the conditional cross-moment does not vanish. A comparison with empirical results confirms these bounds. Simulations show that the estimator is quite robust to additive Gaussian distributed noise. Also through simulations, we show that the error level induced by a rough approximation of the presence indicator shows a strong similarity with that of additive noise. The robustness, with respect both to noise and to inaccuracies in the prior information about the source presence, guarantees a wide applicability of the proposed method.},
  author       = {Phlypo, Ronald and Zarzoso, Vicente and Lemahieu, Ignace},
  issn         = {1053-587X},
  journal      = {IEEE TRANSACTIONS ON SIGNAL PROCESSING},
  keywords     = {constrast functions,estimation,Signal Processing,Conditional likelihood,source extraction,INDEPENDENT COMPONENT ANALYSIS,BLIND SIGNAL EXTRACTION,SOURCE SEPARATION,MAXIMUM-LIKELIHOOD,CONSTRAINED ICA,DICTIONARIES,ALGORITHMS,CRITERIA,MIXTURE},
  language     = {eng},
  number       = {1},
  pages        = {305--316},
  title        = {Source extraction by maximizing the variance in the conditional distribution tails},
  url          = {http://dx.doi.org/10.1109/TSP.2009.2030857},
  volume       = {58},
  year         = {2010},
}

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