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dc.contributor.authorHouégnigan, Ludwig
dc.contributor.authorSafari, Pooyan
dc.contributor.authorNadeu Camprubí, Climent
dc.contributor.authorVan der Schaar, Mike Connor Roger Malcolm
dc.contributor.authorAndré, Michel
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament de Teoria del Senyal i Comunicacions
dc.contributor.otherCentre Tecnològic de Vilanova i la Geltrú
dc.date.accessioned2018-03-01T13:54:24Z
dc.date.available2018-03-01T13:54:24Z
dc.date.issued2017
dc.identifier.citationHouegnigan, L., Safari, P., Nadeu, C., Van Der Schaar, M., Andre, M. A novel approach to real-time range estimation of underwater acoustic sources using supervised machine learning. A: OCEANS IEEE/MTS Aberdeen. "OCEANS 2017 - Aberdeen: 19-22 June 2017". Institute of Electrical and Electronics Engineers (IEEE), 2017, p. 1-5.
dc.identifier.isbn978-1-5090-5278-3
dc.identifier.urihttp://hdl.handle.net/2117/114690
dc.description© 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
dc.description.abstractThe proposed paper introduces a novel method for range estimation of acoustic sources, both cetaceans and industrial sources, in deep sea environments using supervised learning with neural networks in the contex of a single sensor, a compact array, or a small aperture towed array. The presented results have potential both for industrial impact and for the conservation and density estimation of cetaceans. With an average error of 4.3% for ranges up to 8 kilometers and typically below 300 meters, those results are challenging and to our knowledge they are unprecedented for an automated real-time solution.
dc.format.extent5 p.
dc.language.isoeng
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.subjectÀrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Aprenentatge automàtic
dc.subjectÀrees temàtiques de la UPC::Física::Acústica::Sons subaquàtics
dc.subject.lcshMachine learning
dc.subject.lcshUnderwater acoustics
dc.subject.otherRange estimation
dc.subject.otherNeural networks
dc.subject.otherSource localization
dc.subject.otherArray processing
dc.subject.otherAcoustics
dc.subject.otherDensity estimation
dc.titleA novel approach to real-time range estimation of underwater acoustic sources using supervised machine learning
dc.typeConference lecture
dc.subject.lemacAprenentatge automàtic
dc.subject.lemacAcústica submarina
dc.contributor.groupUniversitat Politècnica de Catalunya. VEU - Grup de Tractament de la Parla
dc.contributor.groupUniversitat Politècnica de Catalunya. LAB - Laboratori d'Aplicacions Bioacústiques
dc.identifier.doi10.1109/OCEANSE.2017.8084914
dc.description.peerreviewedPeer Reviewed
dc.relation.publisherversionhttp://ieeexplore.ieee.org/document/8084914/
dc.rights.accessOpen Access
drac.iddocument21892403
dc.description.versionPostprint (author's final draft)
dc.relation.projectidinfo:eu-repo/grantAgreement/MINECO/1PE/TEC2015-69266-P
upcommons.citation.authorHouegnigan, L., Safari, P., Nadeu, C., Van Der Schaar, M., Andre, M.
upcommons.citation.contributorOCEANS IEEE/MTS Aberdeen
upcommons.citation.publishedtrue
upcommons.citation.publicationNameOCEANS 2017 - Aberdeen: 19-22 June 2017
upcommons.citation.startingPage1
upcommons.citation.endingPage5


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