Automated detection of wolf howls using audio spectrogram transformers

Scientific Reports. 2025. Vol. 15. Article 26641.

Makarov N., Savchenko A., Zemtsova I., Novopoltsev M., Poyarkov A., Viricheva A., Chistopolova M., Nikol’skii A., Hernandez-Blanco J.

The grey wolf (Canis lupus) is a pivotal species for ecological studies. As a key participant in ecosystem
processes, it also serves as a model for investigating social structure formation and ecological
adaptation. However, the species’ complex social behavior, spatial dynamics, and expansive habitats
make monitoring and population assessments across large areas particularly challenging. In recent
years, audio traps have been used to collect extensive datasets of wolf vocalizations, particularly
howls. Yet, manually detecting wolf howls in lengthy recordings remains a labor-intensive and
inefficient task. We propose an approach leveraging modern machine-learning techniques to address
this challenge. Following a comprehensive analysis of sound processing methods, we developed two
state-of-the-art deep learning models based on the Audio Spectrogram Transformer architecture. The
first model classifies audio for the presence of animal sounds with a precision of 98.3% and a recall
of 99.3%. The second model distinguishes wolf howls from other animal sounds with a precision of
89.6% and a recall of 93.4%. These models significantly enhance the efficiency and accuracy of wolf
vocalization detection, supporting ecological monitoring and research efforts.

Keywords: ОБРАБОТКА СИГНАЛОВ, глубокое обучение, signal processing, Биоакустика, Bioacoustics, биомониторинг, transformers, трансформеры, Wildlife monitoring, deep learning