ArticleScientific reports2021
An open access dataset for developing automated detectors of Antarctic baleen whale sounds and performance evaluation of two commonly used detectors.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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7 citing papers in PubMed, 35 citations in OpenAlex.
- Searching for separation between frequency-modulated calls of blue and fin whales.Scientific reports · 2026Article
- An open-access dataset of odontocete acoustic signals from the Madeira archipelago.Scientific data · 2026Article
- Automated detection of stereotyped animal sounds using data augmentation and transfer learning.Scientific reports · 2026Article
- Year-round acoustic presence of fin whales southwest of Svalbard suggests mixed-use habitat for feeding and breeding.Scientific reports · 2025Article
- Multiscale Sample Entropy-Based Feature Extraction with Gaussian Mixture Model for Detection and Classification of Blue Whale Vocalization.Entropy (Basel, Switzerland) · 2025Article
- Underwater soundscape in Seaview Bay, Antarctica, and triple ascending trill of the leopard seal (Ecology and evolution · 2024Article
- Diel patterns of fin whale 20 Hz acoustic presence in Eastern Antarctic waters.Royal Society open science · 2023Article
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Authors and funding
10 authors at 10 institutions in 8 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Since 2001, hundreds of thousands of hours of underwater acoustic recordings have been made throughout the Southern Ocean south of 60° S. Detailed analysis of the occurrence of marine mammal sounds in these circumpolar recordings could provide novel insights into their ecology, but manual inspection of the entirety of all recordings would be prohibitively time consuming and expensive. Automated signal processing methods have now developed to the point that they can be applied to these data in a cost-effective manner. However training and evaluating the efficacy of these automated signal processing methods still requires a representative annotated library of sounds to identify the true presence and absence of different sound types. This work presents such a library of annotated recordings for the purpose of training and evaluating automated detectors of Antarctic blue and fin whale calls. Creation of the library has focused on the annotation of a representative sample of recordings to ensure that automated algorithms can be developed and tested across a broad range of instruments, locations, environmental conditions, and years. To demonstrate the utility of the library, we characterise the performance of two automated detection algorithms that have been commonly used to detect stereotyped calls of blue and fin whales. The availability of this library will facilitate development of improved detectors for the acoustic presence of Southern Ocean blue and fin whales. It can also be expanded upon to facilitate standardization of subsequent analysis of spatiotemporal trends in call-density of these circumpolar species.
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