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Depedent Gaussian mixture models for source seperation

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Show simple item record Quirós Carretero, Alicia Wilson, Simon P 2012-07-11T15:20:43Z 2012-07-11T15:20:43Z 2011
dc.description peer-reviewed en_US
dc.description.abstract Source separation is a common task in signal processing and is often analogous to factor analysis. In this work we look at a factor analysis model for source separation of multi-spectral image data where prior information about the sources and their dependencies is quantified as a multivariate Gaussian mixture model with an unknown number of factors. Variational Bayes techniques for model parameter estimation are used. The development of this methodology is motivated by the need to bring an efficient solution to the separation of components in the microwave radiation maps to be obtained by the satellite mission Planck which has the objective of uncovering cosmic microwave background radiation. The proposed algorithm successfully incorporates a rich variety of prior information available to us in this problem in contrast to most of the previous work that assumes completely blind separation of the sources. Results on realistic simulations of Planck maps and on WMAP 5th year images are shown. The technique suggested is easily applicable to other source separation applications by modifying some of the priors. en_US
dc.language.iso eng en_US
dc.relation.ispartofseries 19th European Signal Processing Conference (EUSIPCO 2011);
dc.subject signal processing en_US
dc.subject Gaussain en_US
dc.subject cosmic microwave background (CMB) en_US
dc.title Depedent Gaussian mixture models for source seperation en_US
dc.type info:eu-repo/semantics/conferenceObject en_US
dc.type.supercollection all_ul_research en_US
dc.type.supercollection ul_published_reviewed en_US
dc.contributor.sponsor STATICA en_US
dc.contributor.sponsor SFI en_US
dc.relation.projectid 08/IN.1/I1879 en_US
dc.rights.accessrights info:eu-repo/semantics/openAccess en_US

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