ArticleScientific reports2023
Global birdsong embeddings enable superior transfer learning for bioacoustic classification.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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21 citing papers in PubMed, 122 citations in OpenAlex.
- An Automated Pipeline for Few-Shot Bird Call Classification, a Case Study With the Tooth-Billed Pigeon.Ecology and evolution · 2026Article
- Listening forward: emerging roles of bioacoustics in ecology, evolution, and conservation.Biologia futura · 2026Review
- Design Patterns for the Development and Implementation of Bioacoustic Deep Learning Recognizers.Ecology and evolution · 2026Article
- Twelve quick tips for applying deep learning to animal sounds.PLoS computational biology · 2026Article
- Structure-aware acoustic scene classification: a feature decoupling framework using HPSS and asymmetric convolutions.Scientific reports · 2026Article
- Detection, communication, and individual identification with deep audio embeddings: A case study with North Atlantic right whales.PLoS computational biology · 2026Article
- TweetyBERT: Automated parsing of birdsong through self-supervised machine learning.Patterns (New York, N.Y.) · 2026Article
- Passive Acoustic Data as Phenological Distributions: Uncovering Signals of Temporal Ecology.Ecology and evolution · 2026Article
- A deep learning pipeline for age prediction from vocalisations of the domestic feline.Scientific reports · 2025Article
- Species-independent analysis and identification of emotional animal vocalizations.Scientific reports · 2025Article
- Bioacoustics as a Measure of Population Size and Breeding Success of European Storm PetrelsEcology and evolution · 2025Article
- Automated Detection of Gibbon Calls From Passive Acoustic Monitoring Data Using Convolutional Neural Networks in the "Torch for R" Ecosystem.Ecology and evolution · 2025Article
- Comparing point counts, passive acoustic monitoring, citizen science and machine learning for bird species monitoring in the Mount Kenya ecosystem.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2025Article
- Using tropical reef, bird and unrelated sounds for superior transfer learning in marine bioacoustics.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2025Article
- Circadian rhythms and the use of transfer learning for critically endangered crested argusPhilosophical transactions of the Royal Society of London. Series B, Biological sciences · 2025Article
- Impact of transfer learning methods and dataset characteristics on generalization in birdsong classification.Scientific reports · 2025Article
- AI carves out a niche in ecology and conservation research.Nature methods · 2025Article
- Unlocking the soundscape of coral reefs with artificial intelligence: pretrained networks and unsupervised learning win out.PLoS computational biology · 2025Article
- Visualization and quantification of coral reef soundscapes using CoralSoundExplorer software.PLoS computational biology · 2025Article
- Automated Vibroacoustic Monitoring of Trees for Borer Infestation.Sensors (Basel, Switzerland) · 2024Article
Corrections and comments
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Authors and funding
4 authors at 4 institutions in 3 countries.
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Abstract
Automated bioacoustic analysis aids understanding and protection of both marine and terrestrial animals and their habitats across extensive spatiotemporal scales, and typically involves analyzing vast collections of acoustic data. With the advent of deep learning models, classification of important signals from these datasets has markedly improved. These models power critical data analyses for research and decision-making in biodiversity monitoring, animal behaviour studies, and natural resource management. However, deep learning models are often data-hungry and require a significant amount of labeled training data to perform well. While sufficient training data is available for certain taxonomic groups (e.g., common bird species), many classes (such as rare and endangered species, many non-bird taxa, and call-type) lack enough data to train a robust model from scratch. This study investigates the utility of feature embeddings extracted from audio classification models to identify bioacoustic classes other than the ones these models were originally trained on. We evaluate models on diverse datasets, including different bird calls and dialect types, bat calls, marine mammals calls, and amphibians calls. The embeddings extracted from the models trained on bird vocalization data consistently allowed higher quality classification than the embeddings trained on general audio datasets. The results of this study indicate that high-quality feature embeddings from large-scale acoustic bird classifiers can be harnessed for few-shot transfer learning, enabling the learning of new classes from a limited quantity of training data. Our findings reveal the potential for efficient analyses of novel bioacoustic tasks, even in scenarios where available training data is limited to a few samples.
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