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Automated detection of wolf howls using audio spectrogram transformers.

Nikolai Makarov1,2, Andrey Savchenko3,4,5, Iuliia Zemtsova6

  • 1Sber AI, Moscow, 117997, Russia. nikolai.makarov.sc@gmail.com.

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Summary
This summary is machine-generated.

Researchers developed advanced deep learning models to automatically detect grey wolf (Canis lupus) howls from audio recordings. This AI-powered approach significantly improves the efficiency and accuracy of wildlife monitoring for this pivotal species.

Keywords:
BioacousticsDeep learningSignal processingTransformerWildlife monitoring

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Area of Science:

  • Ecology
  • Bioacoustics
  • Artificial Intelligence

Background:

  • Grey wolves (Canis lupus) are ecologically significant but challenging to monitor due to their complex behavior and vast habitats.
  • Manual analysis of audio recordings for wolf vocalizations is time-consuming and inefficient.

Purpose of the Study:

  • To develop an automated method for detecting grey wolf howls using machine learning.
  • To improve the efficiency and accuracy of wolf population assessment and ecological research.

Main Methods:

  • Utilized deep learning models, specifically the Audio Spectrogram Transformer architecture.
  • Developed two models: one for general animal sound detection and another for specific wolf howl identification.
  • Processed extensive datasets of wolf vocalizations collected via audio traps.

Main Results:

  • The first model achieved 98.3% precision and 99.3% recall for detecting animal sounds.
  • The second model achieved 89.6% precision and 93.4% recall for identifying wolf howls.
  • Demonstrated significant enhancement in detecting wolf vocalizations.

Conclusions:

  • Machine learning models offer a powerful solution for automated bioacoustic monitoring.
  • The developed models enhance the feasibility of large-scale ecological studies involving grey wolves.
  • This technology supports more effective wildlife conservation and research efforts.