ArticleScientific reports2021
Bioacoustic classification of avian calls from raw sound waveforms with an open-source deep learning architecture.
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 13 papers, 1 of them a synthesis that pooled it.
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Who cites it
13 citing papers in PubMed, 1 synthesis or guideline pooled it, 86 citations in OpenAlex.
- AI-Powered Vocalization Analysis in Poultry: Systematic Review of Health, Behavior, and Welfare Monitoring.Sensors (Basel, Switzerland) · 2025Pooled it
- A Sensor-Based TinyML Acoustic Monitoring System for Edge-Side Animal Sound Recognition on Resource-Constrained Microcontrollers.Sensors (Basel, Switzerland) · 2026Article
- Applications and Considerations of Artificial Intelligence in Veterinary Sciences: A Narrative Review.Veterinary medicine and science · 2025Review
- Elephant Sound Classification Using Deep Learning Optimization.Sensors (Basel, Switzerland) · 2025Article
- Modelling reindeer rut activity using on-animal acoustic recorders and machine learning.Ecology and evolution · 2024Article
- callsync: An R package for alignment and analysis of multi-microphone animal recordings.Ecology and evolution · 2024Article
- From beasts to bytes: Revolutionizing zoological research with artificial intelligence.Zoological research · 2023Review
- Who is calling? Optimizing source identification from marmoset vocalizations with hierarchical machine learning classifiers.Journal of the Royal Society, Interface · 2023Article
- A Review of Automated Bioacoustics and General Acoustics Classification Research.Sensors (Basel, Switzerland) · 2022Review
- Improving Misfire Fault Diagnosis with Cascading Architectures via Acoustic Vehicle Characterization.Sensors (Basel, Switzerland) · 2022Article
- A ResNet attention model for classifying mosquitoes from wing-beating sounds.Scientific reports · 2022Article
- Fast environmental sound classification based on resource adaptive convolutional neural network.Scientific reports · 2022Article
- Review
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The use of autonomous recordings of animal sounds to detect species is a popular conservation tool, constantly improving in fidelity as audio hardware and software evolves. Current classification algorithms utilise sound features extracted from the recording rather than the sound itself, with varying degrees of success. Neural networks that learn directly from the raw sound waveforms have been implemented in human speech recognition but the requirements of detailed labelled data have limited their use in bioacoustics. Here we test SincNet, an efficient neural network architecture that learns from the raw waveform using sinc-based filters. Results using an off-the-shelf implementation of SincNet on a publicly available bird sound dataset (NIPS4Bplus) show that the neural network rapidly converged reaching accuracies of over 65% with limited data. Their performance is comparable with traditional methods after hyperparameter tuning but they are more efficient. Learning directly from the raw waveform allows the algorithm to select automatically those elements of the sound that are best suited for the task, bypassing the onerous task of selecting feature extraction techniques and reducing possible biases. We use publicly released code and datasets to encourage others to replicate our results and to apply SincNet to their own datasets; and we review possible enhancements in the hope that algorithms that learn from the raw waveform will become useful bioacoustic tools.
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