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dc.contributor.authorLópez Molina, Carlos Alejandro
dc.contributor.authorCabrera Estanyol, Ferran de
dc.contributor.authorRiba Sagarra, Jaume
dc.contributor.otherUniversitat Politècnica de Catalunya. Doctorat en Teoria del Senyal i Comunicacions
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament de Teoria del Senyal i Comunicacions
dc.date.accessioned2021-06-10T07:50:25Z
dc.date.available2021-06-10T07:50:25Z
dc.date.issued2020
dc.identifier.citationLópez, C.; De Cabrera, F.; Riba, J. Estimation of information in parallel Gaussian channels via model order selection. A: IEEE International Conference on Acoustics, Speech, and Signal Processing. "2020 IEEE International Conference on Acoustics, Speech,and Signal Processing: proceedings: May 4-8, 2020: Centre de Convencions Internacional de Barcelona (CCIB) Barcelona, Spain". Institute of Electrical and Electronics Engineers (IEEE), 2020, p. 5675-5679. ISBN 978-1-5090-6632-2. DOI 10.1109/ICASSP40776.2020.9053506.
dc.identifier.isbn978-1-5090-6632-2
dc.identifier.urihttp://hdl.handle.net/2117/347003
dc.description.abstractWe study the problem of estimating the overall mutual information in M independent parallel discrete-time memory-less Gaussian channels from N independent data sample pairs per channel (inputs and outputs). We focus on the case where the number of active channels L is sparse in comparison with the total number of channels (L ≪ M), for which the direct application of the maximum likelihood principle is problematic due to overfitting, especially for moderate to small N. For this regime, we show that the bias of the mutual information estimate is reduced by resorting to the minimum description length (MDL) principle. As a result, simple pre-processing based on a per-channel threshold on the empirical squared correlation coefficient is required with a fixed threshold that monotonically decreases with N as 1 - N -1/N , for N ≥ 4. The resulting improvement is shown in terms of the estimated information bias.
dc.description.sponsorshipThis work is supported by projects TEC2016-76409-C2-1-R (WINTER), Ministerio de Economia y Competividad, Spanish National Research Plan, and 2017 SGR 578 - AGAUR, Catalan Government.
dc.format.extent5 p.
dc.language.isoeng
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.subjectÀrees temàtiques de la UPC::Enginyeria de la telecomunicació::Processament del senyal::Processament de la parla i del senyal acústic
dc.subject.lcshSpeech processing systems
dc.subject.otherMin
dc.subject.otherDescription length (MDL)
dc.subject.otherBayesian Information Criterion (BIC)
dc.subject.otherLocally Most Powerful Invariant Test (LMPIT)
dc.subject.otherMaximum likelihood (ML)
dc.subject.otherSquared pearson coefficient
dc.subject.otherMutual Information (MI)
dc.subject.otherGeneralized Likelihood Ratio Test (GLRT)
dc.titleEstimation of information in parallel Gaussian channels via model order selection
dc.typeConference lecture
dc.subject.lemacProcessament de la parla
dc.contributor.groupUniversitat Politècnica de Catalunya. SPCOM - Grup de Recerca de Processament del Senyal i Comunicacions
dc.identifier.doi10.1109/ICASSP40776.2020.9053506
dc.description.peerreviewedPeer Reviewed
dc.relation.publisherversionhttps://ieeexplore.ieee.org/document/9053506
dc.rights.accessOpen Access
local.identifier.drac31785403
dc.description.versionPostprint (author's final draft)
dc.relation.projectidinfo:eu-repo/grantAgreement/MINECO/1PE/TEC2016-76409-C2-1-R
dc.relation.projectidinfo:eu-repo/grantAgreement/AGAUR/PRI2017-2019/2017 SGR 578
local.citation.authorLópez, C.; De Cabrera, F.; Riba, J.
local.citation.contributorIEEE International Conference on Acoustics, Speech, and Signal Processing
local.citation.publicationName2020 IEEE International Conference on Acoustics, Speech,and Signal Processing: proceedings: May 4-8, 2020: Centre de Convencions Internacional de Barcelona (CCIB) Barcelona, Spain
local.citation.startingPage5675
local.citation.endingPage5679


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