ArticleScientific reports2020
Deep neural networks for automated detection of marine mammal species.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 42 papers, 1 of them a synthesis that pooled it.
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Who cites it
42 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Analyzing the enhancement of CNN-YOLO and transformer based architectures for real-time animal detection in complex ecological environments.Scientific reports · 2025Pooled it
- Quality-Controlled Generative Augmentation for North Atlantic Right Whale Upcall Detection Using Contour 1D-VAE.Sensors (Basel, Switzerland) · 2026Article
- Deep Audio Detection Networks From Passive Acoustic Monitoring of a Critically Endangered Primate.Integrative zoology · 2026Article
- Automated detection of stereotyped animal sounds using data augmentation and transfer learning.Scientific reports · 2026Article
- Contrastive learning for passive acoustic monitoring: A framework for sound source discovery and cross-site comparison in marine soundscapes.PLoS computational biology · 2026Article
- Automated Classification of Humpback Whale Calls Using Deep Learning: A Comparative Study of Neural Architectures and Acoustic Feature Representations.Sensors (Basel, Switzerland) · 2026Article
- Advancing biological taxonomy in the AI era: deep learning applications, challenges, and future directions.Science China. Life sciences · 2026Review
- Automating timeScientific reports · 2025Article
- A Public Dataset of Annotated Orcinus orca Acoustic Signals for Detection and Ecotype Classification.Scientific data · 2025Article
- Integrating cultural dimensions in sperm whale (Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2025Review
- Multiclass CNN Approach for Automatic Classification of Dolphin Vocalizations.Sensors (Basel, Switzerland) · 2025Article
- Continental-scale behavioral response of birds to a total solar eclipse.Scientific reports · 2025Article
- Automatic detection for bioacoustic research: a practical guide from and for biologists and computer scientists.Biological reviews of the Cambridge Philosophical Society · 2025Review
- Evaluating the performance of automated detection systems for long-term monitoring of delphinids in diverse marine soundscapes.PloS one · 2025Article
- Machine learning for modeling North Atlantic right whale presence to support offshore wind energy development in the U.S. Mid-Atlantic.Scientific reports · 2024Article
- Exploring the landscape of automated species identification apps: Development, promise, and user appraisal.Bioscience · 2024Review
- Underwater soundscape in Seaview Bay, Antarctica, and triple ascending trill of the leopard seal (Ecology and evolution · 2024Article
- Extensive data engineering to the rescue: building a multi-species katydid detector from unbalanced, atypical training datasets.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2024Article
- Remote sensing image information extraction based on Compensated Fuzzy Neural Network and big data analytics.BMC medical imaging · 2024Article
- Propagating variational model uncertainty for bioacoustic call label smoothing.Patterns (New York, N.Y.) · 2024Article
Corrections and comments
- Erratum issued
- Erratum issued
Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Deep neural networks have advanced the field of detection and classification and allowed for effective identification of signals in challenging data sets. Numerous time-critical conservation needs may benefit from these methods. We developed and empirically studied a variety of deep neural networks to detect the vocalizations of endangered North Atlantic right whales (Eubalaena glacialis). We compared the performance of these deep architectures to that of traditional detection algorithms for the primary vocalization produced by this species, the upcall. We show that deep-learning architectures are capable of producing false-positive rates that are orders of magnitude lower than alternative algorithms while substantially increasing the ability to detect calls. We demonstrate that a deep neural network trained with recordings from a single geographic region recorded over a span of days is capable of generalizing well to data from multiple years and across the species' range, and that the low false positives make the output of the algorithm amenable to quality control for verification. The deep neural networks we developed are relatively easy to implement with existing software, and may provide new insights applicable to the conservation of endangered species.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.