Show simple item record

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.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.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.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.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.description.peerreviewedPeer Reviewed
dc.rights.accessOpen Access
dc.description.versionPostprint (author's final draft)
local.citation.authorHouegnigan, L.; Safari, P.; Nadeu, C.; Van Der Schaar, M.; Andre, M.
local.citation.contributorOCEANS IEEE/MTS Aberdeen
local.citation.publicationNameOCEANS 2017 - Aberdeen: 19-22 June 2017

Files in this item


This item appears in the following Collection(s)

Show simple item record

All rights reserved. This work is protected by the corresponding intellectual and industrial property rights. Without prejudice to any existing legal exemptions, reproduction, distribution, public communication or transformation of this work are prohibited without permission of the copyright holder