Title:
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Machine and deep learning approaches to localization and range estimation of underwater acoustic sources
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Author:
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Houégnigan, Ludwig; Safari, Pooyan; Nadeu Camprubí, Climent; André, Michel; Van der Schaar, Mike Connor Roger Malcolm
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Other authors:
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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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This 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. |
Abstract:
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Peer Reviewed |
Subject(s):
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-Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Aprenentatge automàtic -Machine learning -Range estimation -Neural networks -Source localization -Array processing -Acoustics -Deep learning -Aprenentatge automàtic |
Rights:
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Document type:
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Article - Published version Conference Object |
Published by:
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Institute of Electrical and Electronics Engineers (IEEE)
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