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A novel approach to real-time range estimation of underwater acoustic sources using supervised machine learning
| dc.contributor.author | Houégnigan, Ludwig |
| dc.contributor.author | Safari, Pooyan |
| dc.contributor.author | Nadeu Camprubí, Climent |
| dc.contributor.author | Van der Schaar, Mike Connor Roger Malcolm |
| dc.contributor.author | André, Michel |
| dc.contributor.other | Universitat Politècnica de Catalunya. Departament de Teoria del Senyal i Comunicacions |
| dc.contributor.other | Centre Tecnològic de Vilanova i la Geltrú |
| dc.date.accessioned | 2018-03-01T13:54:24Z |
| dc.date.available | 2018-03-01T13:54:24Z |
| dc.date.issued | 2017 |
| dc.identifier.citation | Houegnigan, 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.isbn | 978-1-5090-5278-3 |
| dc.identifier.uri | http://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.abstract | The 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.extent | 5 p. |
| dc.language.iso | eng |
| dc.publisher | Institute of Electrical and Electronics Engineers (IEEE) |
| dc.subject | Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Aprenentatge automàtic |
| dc.subject | Àrees temàtiques de la UPC::Física::Acústica::Sons subaquàtics |
| dc.subject.lcsh | Machine learning |
| dc.subject.lcsh | Underwater acoustics |
| dc.subject.other | Range estimation |
| dc.subject.other | Neural networks |
| dc.subject.other | Source localization |
| dc.subject.other | Array processing |
| dc.subject.other | Acoustics |
| dc.subject.other | Density estimation |
| dc.title | A novel approach to real-time range estimation of underwater acoustic sources using supervised machine learning |
| dc.type | Conference lecture |
| dc.subject.lemac | Aprenentatge automàtic |
| dc.subject.lemac | Acústica submarina |
| dc.contributor.group | Universitat Politècnica de Catalunya. VEU - Grup de Tractament de la Parla |
| dc.contributor.group | Universitat Politècnica de Catalunya. LAB - Laboratori d'Aplicacions Bioacústiques |
| dc.identifier.doi | 10.1109/OCEANSE.2017.8084914 |
| dc.description.peerreviewed | Peer Reviewed |
| dc.relation.publisherversion | http://ieeexplore.ieee.org/document/8084914/ |
| dc.rights.access | Open Access |
| local.identifier.drac | 21892403 |
| dc.description.version | Postprint (author's final draft) |
| dc.relation.projectid | info:eu-repo/grantAgreement/MINECO//TEC2015-69266-P/ES/TECNOLOGIAS DE APRENDIZAJE PROFUNDO APLICADAS AL PROCESADO DE VOZ Y AUDIO/ |
| local.citation.author | Houegnigan, L.; Safari, P.; Nadeu, C.; Van Der Schaar, M.; Andre, M. |
| local.citation.contributor | OCEANS IEEE/MTS Aberdeen |
| local.citation.publicationName | OCEANS 2017 - Aberdeen: 19-22 June 2017 |
| local.citation.startingPage | 1 |
| local.citation.endingPage | 5 |
