Abstract

The benefit of passive acoustic monitoring is indisputable; it is non-invasive to marine life and can capture vast quantities of data over large stretches of space and time. The limiting factor of analysing large volumes of data quickly overwhelms the analyst, and by utilising some form of automatic signal identification this process can be substantially faster. Here we compare the performance of a deep learning detector, simple correlation, and a more traditional energy detector (or the fin whale index). Here we compare three detectors on data from the Lofoten-Vesterålen Observatory in Norway (northern Atlantic Ocean), and the Comprehensive Nuclear-Test-Ban-Treaty Organisations hydrophone station at Ascension Island (southern Atlantic Ocean).

Original languageEnglish
Title of host publicationProceedings of the 29th International Congress on Sound and Vibration, ICSV 2023
EditorsEleonora Carletti
PublisherSociety of Acoustics
Number of pages7
ISBN (Electronic)9788011034238
Publication statusPublished - 13 Jul 2023
Event29th International Congress on Sound and Vibration, ICSV 2023 - Prague, Czech Republic
Duration: 9 Jul 202313 Jul 2023

Publication series

NameProceedings of the International Congress on Sound and Vibration
ISSN (Electronic)2329-3675

Conference

Conference29th International Congress on Sound and Vibration, ICSV 2023
Country/TerritoryCzech Republic
CityPrague
Period9/07/2313/07/23

Bibliographical note

Publisher Copyright:
© 2023 British Crown Owned Copyright/Dstl. Contains public sector information licensed under the Open Government Licence v3.0.

Keywords

  • detectors
  • long-term measurements
  • marine acoustics

ASJC Scopus subject areas

  • Acoustics and Ultrasonics

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