ArticleScientific reports2019
Deep Machine Learning Techniques for the Detection and Classification of Sperm Whale Bioacoustics.
Article in Scientific reports, 2019. 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 22 papers, 1 of them a synthesis that pooled it.
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Who cites it
22 citing papers in PubMed, 1 synthesis or guideline pooled it.
- AI-Powered Vocalization Analysis in Poultry: Systematic Review of Health, Behavior, and Welfare Monitoring.Sensors (Basel, Switzerland) · 2025Pooled it
- Deep Audio Detection Networks From Passive Acoustic Monitoring of a Critically Endangered Primate.Integrative zoology · 2026Article
- Automated Detection and Classification of Marine Species Vocalizations Using a YOLO-Based Deep Learning Framework.Ecology and evolution · 2026Article
- Leveraging artificial intelligence in bioacoustics for animal health monitoring and early diagnosis in veterinary medicine.Frontiers in veterinary science · 2026Review
- Deep learning-based morphological analysis of human sperm.Medical & biological engineering & computing · 2026Article
- Automatic detection for bioacoustic research: a practical guide from and for biologists and computer scientists.Biological reviews of the Cambridge Philosophical Society · 2025Review
- Multiscale Sample Entropy-Based Feature Extraction with Gaussian Mixture Model for Detection and Classification of Blue Whale Vocalization.Entropy (Basel, Switzerland) · 2025Article
- Evaluating the performance of automated detection systems for long-term monitoring of delphinids in diverse marine soundscapes.PloS one · 2025Article
- Elephants and algorithms: a review of the current and future role of AI in elephant monitoring.Journal of the Royal Society, Interface · 2023Review
- Towards automated long-term acoustic monitoring of endangered river dolphins: a case study in the Brazilian Amazon floodplains.Scientific reports · 2023Article
- Review
- There You Are! Automated Detection of Indris' Songs on Features Extracted from Passive Acoustic Recordings.Animals : an open access journal from MDPI · 2023Article
- A Review of Automated Bioacoustics and General Acoustics Classification Research.Sensors (Basel, Switzerland) · 2022Review
- Toward understanding the communication in sperm whales.iScience · 2022Review
- Convolutional Neural Networks for the Identification of African Lions from Individual Vocalizations.Journal of imaging · 2022Article
- BioCPPNet: automatic bioacoustic source separation with deep neural networks.Scientific reports · 2021Article
- Comparing recurrent convolutional neural networks for large scale bird species classification.Scientific reports · 2021Article
- Bioacoustic classification of avian calls from raw sound waveforms with an open-source deep learning architecture.Scientific reports · 2021Article
- Improve automatic detection of animal call sequences with temporal context.Journal of the Royal Society, Interface · 2021Article
- Seismic localization of elephant rumbles as a monitoring approach.Journal of the Royal Society, Interface · 2021Article
Corrections and comments
- Erratum issued
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We implemented Machine Learning (ML) techniques to advance the study of sperm whale (Physeter macrocephalus) bioacoustics. This entailed employing Convolutional Neural Networks (CNNs) to construct an echolocation click detector designed to classify spectrograms generated from sperm whale acoustic data according to the presence or absence of a click. The click detector achieved 99.5% accuracy in classifying 650 spectrograms. The successful application of CNNs to clicks reveals the potential of future studies to train CNN-based architectures to extract finer-scale details from cetacean spectrograms. Long short-term memory and gated recurrent unit recurrent neural networks were trained to perform classification tasks, including (1) "coda type classification" where we obtained 97.5% accuracy in categorizing 23 coda types from a Dominica dataset containing 8,719 codas and 93.6% accuracy in categorizing 43 coda types from an Eastern Tropical Pacific (ETP) dataset with 16,995 codas; (2) "vocal clan classification" where we obtained 95.3% accuracy for two clan classes from Dominica and 93.1% for four ETP clan types; and (3) "individual whale identification" where we obtained 99.4% accuracy using two Dominica sperm whales. These results demonstrate the feasibility of applying ML to sperm whale bioacoustics and establish the validity of constructing neural networks to learn meaningful representations of whale vocalizations.
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