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Deep learning framework detects millions of fin whale calls from seismic data

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a deep learning framework for detecting fin whale calls in ocean-bottom seismometer recordings. This method, trained on hydrophone data, successfully generalized to seismometer data from a different region without retraining. Applied to over 378,000 hours of recordings, the system identified 6.3 million calls with high precision, creating the largest fin whale call catalog to date and enabling ecological analysis of seasonal shifts and basin-scale activity patterns. AI

IMPACT This research demonstrates a novel application of deep learning for ecological monitoring, potentially enabling large-scale species tracking with existing geophysical infrastructure.

RANK_REASON The cluster contains an academic paper detailing a new deep learning framework for bioacoustic detection. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Deep learning framework detects millions of fin whale calls from seismic data

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The cluster contains an academic paper detailing a new deep learning framework for bioacoustic detection. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Jocelyn Japnanto, Alex A. Saoulis, Miriam Romagosa, Rita Leit\~ao, Gabrielle Arrieta, M\'onica A. Silva, Matthew Graham, Ana M. G. Ferreira ·

    Large-scale bioacoustic detection using semantic segmentation: a deep learning framework applied to fin whale calls in ocean-bottom seismometer recordings

    arXiv:2609.13281v1 Announce Type: cross Abstract: Ocean-bottom seismometers (OBS), originally deployed for geophysical research, continuously record low-frequency sound for months to years across broad areas of ocean, offering a largely untapped resource for passive acoustic moni…

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