A NOVEL APPROACH TO REAL-TIME RANGE ESTIMATION OF UNDERWATER ACOUSTIC SOURCES USING SUPERVISED MACHINE LEARNING

被引：0

作者：

Houegnigan, Ludwig ^{[1
]}

Safari, Pooyan ^{[2
]}

Nadeu, Climent ^{[2
]}

van der Schaar, Mike ^{[1
]}

Andre, Michel ^{[1
]}

机构：

[1] Polytech Univ Catalonia UPC, LAB, Barcelona, Spain

[2] Polytech Univ Catalonia UPC, TALP Res Ctr, Dept TSC, Barcelona, Spain

来源：

OCEANS 2017 - ABERDEEN | 2017年

关键词：

range estimation; neural networks; source localization; array processing; acoustics; density estimation; SINGLE HYDROPHONE; SOUND-ABSORPTION; OCEAN MEASUREMENTS; BEAM PATTERN; SEA-WATER; WHALE; LOCALIZATION; DELAYS;

D O I：

暂无

中图分类号：

O42 [声学];

学科分类号：

070206 ; 082403 ;

摘要：

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.

引用

页数：5

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