Adaptive methods for score function modeling in blind source separation

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dc.contributor Aalto-yliopisto fi
dc.contributor Aalto University en
dc.contributor.author Karvanen, Juha
dc.date.accessioned 2012-02-10T09:32:16Z
dc.date.available 2012-02-10T09:32:16Z
dc.date.issued 2002-08-26
dc.identifier.isbn 951-22-5915-X
dc.identifier.issn 1458-6401
dc.identifier.uri https://aaltodoc.aalto.fi/handle/123456789/2215
dc.description.abstract In signal processing and related fields, multichannel measurements are often encountered. Depending on the application, for instance, multiple antennas, multiple microphones or multiple biomedical sensors are used for the data acquisition. Such systems can be described using Multiple-Input Multiple-Output (MIMO) system models. In many cases, several source signals are present at the same time and there is only limited knowledge of their properties and how they contribute to each sensor output. If the source signals and the physical system are unknown and only the sensor outputs are observed, the processing methods developed for recovering the original signals are called blind. In Blind Source Separation (BSS) the goal is to recover the source signals from the observed mixed signals (mixtures). Blindness means that neither the sources nor the mixing system is known. Separation can be based on the theoretically limiting but practically feasible assumption that the sources are statistically independent. This assumption connects BSS and Independent Component Analysis (ICA). The usage of mutual information as a measure of independence leads to iterative estimation of the score functions of the mixtures. The purpose of this thesis is to develop BSS methods that can adapt to different source distributions. Adaptation makes it possible to separate sources without knowing the source distributions or even the characteristics of source distributions. Special attention is paid to methods that allow also asymmetric source distributions. Asymmetric distributions occur in important applications such as communications and biomedical signal processing. Adaptive techniques are proposed for the modeling of score functions or estimating functions. Three approaches based on the Pearson system, the Extended Generalized Lambda Distribution (EGLD) and adaptively combined fixed estimating functions are proposed. The Pearson system and the EGLD are parametric families of distributions and they are used to model the distributions of the mixtures. The strength of these parametric families is that they contain a wide class of distributions, including asymmetric distributions with positive and negative kurtosis, while the estimation of the parameters is still a relatively simple procedure. The methods may be implemented using existing ICA algorithms. The reliable performance of the proposed methods is demonstrated in extensive simulations. In addition to symmetric source distributions, asymmetric distributions, such as Rayleigh and lognormal distribution, are utilized in simulations. The score adaptive methods outperform commonly used methods due to their ability to adapt to asymmetric distributions. en
dc.format.extent 67, [59]
dc.format.mimetype application/pdf
dc.language.iso en en
dc.publisher Helsinki University of Technology en
dc.publisher Teknillinen korkeakoulu fi
dc.relation.ispartofseries Report / Helsinki University of Technology, Signal Processing Laboratory en
dc.relation.ispartofseries 35 en
dc.relation.haspart J. Karvanen, J. Eriksson and V. Koivunen. Pearson System based Method for Blind Separation. In Proc. of The Second International Workshop on Independent Component Analysis and Blind Signal Separation, ICA2000, pages 585-590, June 2000.
dc.relation.haspart J. Eriksson, J. Karvanen and V. Koivunen. Source Distribution Adaptive Maximum Likelihood Estimation of ICA Model. In Proc. of The Second International Workshop on Independent Component Analysis and Blind Signal Separation, ICA2000, pages 227-232, June 2000.
dc.relation.haspart J. Karvanen, J. Eriksson and V. Koivunen. Maximum Likelihood Estimation of ICA model for Wide Class of Source Distributions. In Proc. of the 2000 IEEE Workshop on Neural Networks for Signal Processing X, pages 445-454, December 2000.
dc.relation.haspart J. Karvanen and V. Koivunen. Blind Separation of Communication Signals Using Pearson System Based Method. In Proc. of The Thirty-Fifth Annual Conference on Information Sciences and Systems, Volume II, pages 764-767, March 2001.
dc.relation.haspart J. Karvanen and V. Koivunen. Blind Separation Methods Based on Pearson system and its Extensions. Signal Processing Volume 82, Issue 4, pages 663-673, April 2002.
dc.relation.haspart J. Karvanen, J. Eriksson and V. Koivunen. Adaptive Score Functions for Maximum Likelihood ICA. Journal of VLSI Signal Processing, Volume 32, pages 83-92, 2002.
dc.relation.haspart J. Karvanen and V. Koivunen. Blind Separation using Absolute Moments Based Adaptive Estimating Function. In Proc. of the Third International Conference on Independent Component Analysis and Signal Separation, ICA2001, pages 218-223, December 2001.
dc.subject.other Electrical engineering en
dc.title Adaptive methods for score function modeling in blind source separation en
dc.type G5 Artikkeliväitöskirja fi
dc.description.version reviewed en
dc.contributor.department Department of Electrical and Communications Engineering en
dc.contributor.department Sähkö- ja tietoliikennetekniikan osasto fi
dc.subject.keyword blind source separation en
dc.subject.keyword independent component analysis en
dc.subject.keyword Pearson system en
dc.subject.keyword generalized lambda distribution en
dc.subject.keyword method of moments en
dc.subject.keyword L-moments en
dc.subject.keyword kurtosis en
dc.subject.keyword skewness en
dc.identifier.urn urn:nbn:fi:tkk-001854
dc.type.dcmitype text en
dc.type.ontasot Väitöskirja (artikkeli) fi
dc.type.ontasot Doctoral dissertation (article-based) en
dc.contributor.lab Signal Processing Laboratory en
dc.contributor.lab Signaalinkäsittelytekniikan laboratorio fi


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