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dc.contributor.authorHouégnigan, Ludwig
dc.contributor.authorSafari, Pooyan
dc.contributor.authorNadeu Camprubí, Climent
dc.contributor.authorAndré, Michel
dc.contributor.authorVan der Schaar, Mike Connor Roger Malcolm
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament de Teoria del Senyal i Comunicacions
dc.contributor.otherCentre Tecnològic de Vilanova i la Geltrú
dc.date.accessioned2018-06-20T13:55:16Z
dc.date.issued2017
dc.identifier.citationHouegnigan, L., Safari, P., Nadeu, C., Andre, M., Van Der Schaar, M. Machine and deep learning approaches to localization and range estimation of underwater acoustic sources. A: Acoustics in Underwater Geosciences Symposium. "2017 IEEE/OES Acoustics in Underwater Geosciences Symposium (RIO Acoustics 2017): Rio de Janeiro, Brazil: 25-27 July 2017". Institute of Electrical and Electronics Engineers (IEEE), 2017, p. 1-6.
dc.identifier.isbn978-1-5090-5011-6
dc.identifier.urihttp://hdl.handle.net/2117/118230
dc.description.abstractThis paper introduces ongoing experiments and early results for the underwater localization and range estimation of acoustic sources. Beyond classical results obtained for direction of arrival estimation, results concerning range estimation using supervised learning with neural networks having both shallow and deep architectures are presented. The developed method is applicable in the context of a single sensor, a compact array, or a small aperture towed array and provided results with great potential both for industrial impact and for the conservation and density estimation of cetaceans. With an average error of 4.3% and 3.5%-respectively for a shallow and for a deep pre-trained architecture-for ranges up to 8 kilometers and consistently below 300 meters, the system provides robust estimates suitable for an automated real-time solution.
dc.format.extent6 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.subject.lcshMachine learning
dc.subject.otherRange estimation
dc.subject.otherNeural networks
dc.subject.otherSource localization
dc.subject.otherArray processing
dc.subject.otherAcoustics
dc.subject.otherDeep learning
dc.titleMachine and deep learning approaches to localization and range estimation of underwater acoustic sources
dc.typeConference lecture
dc.subject.lemacAprenentatge automàtic
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/RIOAcoustics.2017.8349716
dc.description.peerreviewedPeer Reviewed
dc.relation.publisherversionhttps://ieeexplore.ieee.org/document/8349716/
dc.rights.accessRestricted access - publisher's policy
drac.iddocument21892392
dc.description.versionPostprint (published version)
dc.relation.projectidinfo:eu-repo/grantAgreement/MINECO/1PE/TEC2015-69266-P
dc.date.lift10000-01-01
upcommons.citation.authorHouegnigan, L., Safari, P., Nadeu, C., Andre, M., Van Der Schaar, M.
upcommons.citation.contributorAcoustics in Underwater Geosciences Symposium
upcommons.citation.publishedtrue
upcommons.citation.publicationName2017 IEEE/OES Acoustics in Underwater Geosciences Symposium (RIO Acoustics 2017): Rio de Janeiro, Brazil: 25-27 July 2017
upcommons.citation.startingPage1
upcommons.citation.endingPage6


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