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https://doi.org/10.25144/22225...
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AUTOMATIC IDENTIFICATION OF BIOPHONICS AND SEA ICE PROCESSES IN LARGE DATASETS FROM THE HIGH ARCTIC OCEAN

Authors: Cleverly, Jonathan; Blondel, Philippe; Sagen, Hanne; Dzieciuch, Matt; Storheim, Espen;

AUTOMATIC IDENTIFICATION OF BIOPHONICS AND SEA ICE PROCESSES IN LARGE DATASETS FROM THE HIGH ARCTIC OCEAN

Abstract

The extent of sea ice in the Arctic Ocean has severely declined due to the amplification of climate change. Sea ice plays a pivotal role in the ice-albedo feedback process, limiting the planet’s absorption of solar radiation. Its reduction has been linked to rising sea levels and extreme weather events at more temperate latitudes. Many species are strongly associated with seasonal variation of ice cover, primarily for feeding grounds. With passive acoustic monitoring, acoustic signatures of marine mammals and sea ice processes can be identified to further understand the state of the Arctic.This study uses recordings from the long-term research projects “Acoustic Ocean Under Melting Ice” (UNDER-ICE, 2014-2016) and “Coordinated Arctic Acoustic Thermometry Experiment” (CAATEX, 2019-2020). UNDER-ICE, in the Fram Strait, recorded for 130 seconds every 3 hours, at 1,953 Hertz from September 2014 to March 2016, whereas CAATEX, along the Eurasian Basin, recorded for 45 minutes every 12 hours at 976 Hertz from August 2019 to August 2020. The selection of recording stations presented here corresponds to 50+ GB of data, encompassing a wide range of soundscapes and acoustic sources (including ships and geophonics).Due to the high duty cycle, these large datasets require automatic processing to highlight processes or signals of interest. This study will consider conventional detection methods, such as energy sum detectors, alongside the potential use of acoustic indices (such as Acoustic Complexity Index or Entropy) for identifying significant audio files for further analysis. Biophonic and cryophonic soundscape contributions will be linked with their seasonal variations, environmental changes in the region and future uses to train AI approaches for large-scale measurements. The usability of acoustic indices in analysing low-sample rate data (relative to the terrestrial ecological studies from which they were derived) will also be discussed.

Country
United Kingdom
Keywords

/dk/atira/pure/subjectarea/asjc/1700/1702; name=Artificial Intelligence, underwater acoustics, /dk/atira/pure/subjectarea/asjc/3100/3102; name=Acoustics and Ultrasonics, climate change, Arctic, /dk/atira/pure/sustainabledevelopmentgoals/life_below_water; name=SDG 14 - Life Below Water, /dk/atira/pure/sustainabledevelopmentgoals/climate_action; name=SDG 13 - Climate Action, sea ice, biodiversity

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
Green