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Bioacoustic Detection of Wolves Using AI (BirdNET, Cry-Wolf and BioLingual).

Johanne Holm Jacobsen1, Pietro Orlando2, Line Østergaard Jensen1

  • 1Department of Chemistry and Bioscience, Aalborg University, 9220 Aalborg, Denmark.

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|January 28, 2026
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Summary
This summary is machine-generated.

Artificial Intelligence (AI) methods can efficiently detect wolf howls from audio recordings, significantly aiding wolf population monitoring. A combined AI approach achieved 96.2% recall, complementing traditional methods for conservation.

Keywords:
Canis lupusacoustic monitoringbioacousticswolf howls

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Area of Science:

  • Wildlife biology
  • Bioacoustics
  • Artificial Intelligence in ecology

Background:

  • Increasing wolf (Canis lupus) populations necessitate advanced monitoring techniques.
  • Traditional wolf monitoring methods are resource-intensive and often insufficient for current population sizes.

Purpose of the Study:

  • To evaluate Artificial Intelligence (AI) methods for detecting and classifying wolf howls from acoustic recordings.
  • To assess the effectiveness of AI in improving wolf population monitoring compared to traditional approaches.

Main Methods:

  • Three AI models (BirdNET, Cry-Wolf, BioLingual) were tested on acoustic data from Denmark.
  • Data were collected using Song Meter SM4 (SM4) audio recorders.
  • Manual validation established a ground truth of 260 wolf howls.

Main Results:

  • Individual AI models showed varying recall and precision, with BirdNET achieving the highest recall (78.5%).
  • AI solutions significantly reduced processing time, offering substantial efficiency gains.
  • A combined AI approach achieved a high recall of 96.2% (250/260 howls detected).

Conclusions:

  • AI methods, while not fully autonomous, serve as powerful human-aided data reduction tools.
  • AI offers a scalable, non-invasive complement to traditional wolf monitoring.
  • AI enhances the feasibility of large-scale wolf population research and conservation efforts.