Título:
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A novel approach to real-time range estimation of underwater acoustic sources using supervised machine learning
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Autor/a:
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Houégnigan, Ludwig; Safari, Pooyan; Nadeu Camprubí, Climent; Van der Schaar, Mike Connor Roger Malcolm; André, Michel
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Otros autores:
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Universitat Politècnica de Catalunya. Departament de Teoria del Senyal i Comunicacions; Centre Tecnològic de Vilanova i la Geltrú; Universitat Politècnica de Catalunya. VEU - Grup de Tractament de la Parla; Universitat Politècnica de Catalunya. LAB - Laboratori d'Aplicacions Bioacústiques |
Abstract:
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Abstract:
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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. |
Abstract:
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Peer Reviewed |
Materia(s):
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-Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Aprenentatge automàtic -Àrees temàtiques de la UPC::Física::Acústica::Sons subaquàtics -Machine learning -Underwater acoustics -Range estimation -Neural networks -Source localization -Array processing -Acoustics -Density estimation -Aprenentatge automàtic -Acústica submarina |
Derechos:
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Tipo de documento:
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Artículo - Versión presentada Objeto de conferencia |
Editor:
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Institute of Electrical and Electronics Engineers (IEEE)
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