A novel approach to real-time range estimation of underwater acoustic sources using supervised machine learning

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hdl:2117/114690
Document typeConference lecture
Defense date2017
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Rights accessOpen Access
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ProjectTECNOLOGIAS DE APRENDIZAJE PROFUNDO APLICADAS AL PROCESADO DE VOZ Y AUDIO (MINECO-TEC2015-69266-P)
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.
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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.
ISBN978-1-5090-5278-3
Publisher versionhttp://ieeexplore.ieee.org/document/8084914/
Collections
- LAB - Laboratori d'Aplicacions Bioacústiques - Ponències/Comunicacions de congressos [13]
- Centre Tecnològic de Vilanova i la Geltrú - Ponències/Comunicacions de congressos [26]
- VEU - Grup de Tractament de la Parla - Ponències/Comunicacions de congressos [436]
- Departament de Teoria del Senyal i Comunicacions - Ponències/Comunicacions de congressos [3.190]
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