ArticleJournal of the Royal Society, Interface2021
Improve automatic detection of animal call sequences with temporal context.
Article in Journal of the Royal Society, Interface, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Automated detection of stereotyped animal sounds using data augmentation and transfer learning.Scientific reports · 2026Article
- Automating timeScientific reports · 2025Article
- SeqFusionNet: A hybrid model for sequence-aware and globally integrated acoustic representation.PloS one · 2025Article
- Multi-year soundscape recordings and automated call detection reveals varied impact of moonlight on calling activity of neotropical forest katydids.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 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
- ORCA-SPY enables killer whale sound source simulation, detection, classification and localization using an integrated deep learning-based segmentation.Scientific reports · 2023Article
- Temporal evolution of the Mediterranean fin whale song.Scientific reports · 2022Article
- Automated identification of chicken distress vocalizations using deep learning models.Journal of the Royal Society, Interface · 2022Article
- Review
- A machine learning pipeline for classification of cetacean echolocation clicks in large underwater acoustic datasets.PLoS computational biology · 2021Article
Corrections and comments
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Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Many animals rely on long-form communication, in the form of songs, for vital functions such as mate attraction and territorial defence. We explored the prospect of improving automatic recognition performance by using the temporal context inherent in song. The ability to accurately detect sequences of calls has implications for conservation and biological studies. We show that the performance of a convolutional neural network (CNN), designed to detect song notes (calls) in short-duration audio segments, can be improved by combining it with a recurrent network designed to process sequences of learned representations from the CNN on a longer time scale. The combined system of independently trained CNN and long short-term memory (LSTM) network models exploits the temporal patterns between song notes. We demonstrate the technique using recordings of fin whale (
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