Vocalisations of Antarctic blue whales, Balaenoptera musculus intermedia, recorded during the 2001/2002 and 2002/2003 IWC/SOWER circumpolar cruises, Area V, Antarctica

Rankin, Shannon; Ljungblad, Don; Clark, Christopher G.; Kato, Hirotaka

doi:10.47536/jcrm.v7i1.752

Cited by 66 publications

References 12 publications

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Deep learning algorithm outperforms experienced human observer at detection of blue whale D‐calls: a double‐observer analysis

Miller

Madhusudhana

Aulich

et al. 2022

Remote Sens Ecol Conserv

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An automated algorithm for passive acoustic detection of blue whale D‐calls was developed based on established deep learning methods for image recognition via the DenseNet architecture. The detector was trained on annotated acoustic recordings from the Antarctic, and performance of the detector was assessed by calculating precision and recall using a separate independent dataset also from the Antarctic. Detections from both the human analyst and automated detector were then inspected by an independent judge to identify any calls missed by either approach and to adjudicate whether the apparent false‐positive detections from the automated approach were actually true positives. A final performance assessment was conducted using double‐observer methods (via a closed‐population Huggins mark–recapture model) to assess the probability of detection of calls by both the human analyst and automated detector, based on the assumption of false‐positive‐free adjudicated detections. According to our double‐observer analysis, the automated detector showed superior performance with higher recall and fewer false positives than the original human analyst, and with performance similar to existing top automated detectors. To understand the performance of both detectors we inspected the time‐series and signal‐to‐noise ratio (SNR) of detections for the test dataset, and found that most of the advantages from the automated detector occurred at low and medium SNR.

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Deep learning algorithm outperforms experienced human observer at detection of blue whale D‐calls: a double‐observer analysis

Miller

Madhusudhana

Aulich

et al. 2022

Remote Sens Ecol Conserv

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Deep learning in marine bioacoustics: a benchmark for baleen whale detection

Schall,

Kaya,

Debusschere

et al. 2024

Remote Sens Ecol Conserv

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Passive acoustic monitoring (PAM) is commonly used to obtain year‐round continuous data on marine soundscapes harboring valuable information on species distributions or ecosystem dynamics. This continuously increasing amount of data requires highly efficient automated analysis techniques in order to exploit the full potential of the available data. Here, we propose a benchmark, which consists of a public dataset, a well‐defined task and evaluation procedure to develop and test automated analysis techniques. This benchmark focuses on the special case of detecting animal vocalizations in a real‐world dataset from the marine realm. We believe that such a benchmark is necessary to monitor the progress in the development of new detection algorithms in the field of marine bioacoustics. We ultimately use the proposed benchmark to test three detection approaches, namely ANIMAL‐SPOT, Koogu and a simple custom sequential convolutional neural network (CNN), and report performances. We report the performance of the three detection approaches in a blocked cross‐validation fashion with 11 site‐year blocks for a multi‐species detection scenario in a large marine passive acoustic dataset. Performance was measured with three simple metrics (i.e., true classification rate, noise misclassification rate and call misclassification rate) and one combined fitness metric, which allocates more weight to the minimization of false positives created by noise. Overall, ANIMAL‐SPOT performed the best with an average fitness metric of 0.6, followed by the custom CNN with an average fitness metric of 0.57 and finally Koogu with an average fitness metric of 0.42. The presented benchmark is an important step to advance in the automatic processing of the continuously growing amount of PAM data that are collected throughout the world's oceans. To ultimately achieve usability of developed algorithms, the focus of future work should be laid on the reduction of the false positives created by noise.

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A Review of Blue Whale Studies from HARUphones in the Pacific

Stafford

2016

Modern Acoustics and Signal Processing

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Vocalisations of Antarctic blue whales, Balaenoptera musculus intermedia, recorded during the 2001/2002 and 2002/2003 IWC/SOWER circumpolar cruises, Area V, Antarctica

Cited by 66 publications

References 12 publications

Deep learning algorithm outperforms experienced human observer at detection of blue whale D‐calls: a double‐observer analysis

Deep learning algorithm outperforms experienced human observer at detection of blue whale D‐calls: a double‐observer analysis

Deep learning in marine bioacoustics: a benchmark for baleen whale detection

A Review of Blue Whale Studies from HARUphones in the Pacific

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