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DeepSonic: Deep sea soundscapes from machine learning

CORDIS · observation · Publication date unknown

DeepSonic: Deep sea soundscapes from machine learning Acoustic recordings of ocean noise offer vital insights into marine ecosystems, revealing the impacts of human activities such as noise pollution and climate change. Large baleen whales like blue and fin whales emit low-frequency calls that travel long distances and can be detected by ocean bottom seismometers (OBS)—instruments originally deployed for seismological research. This makes OBSs a cost-effective, passive tool for long-term marine mammal and ocean noise monitoring in deep and remote areas. Traditional whale call detection methods are often inefficient in noisy environments and demand extensive manual effort. To overcome this, we developed new machine learning algorithms—carried out as part of an ERC UPFLOW spin-off project—that work in low-data training regimes to detect whale vocalisations, reverberations, and other signals within the unique UPFLOW OBS datase

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recordType

award

status

SIGNED

region

EU

value

150000

unit

EUR