Living in Harmony With Wolves: Russian Scientists Teach AI to Recognize Wolf Howls
Russian researchers have developed and tested an AI system capable of automatically identifying wolf howls, opening new possibilities for wildlife conservation
Acoustic Data for a Changing Planet
Modern technology is becoming essential for understanding wildlife populations and habitats. Since 1970, global wildlife numbers have fallen by more than 70%. Wolves, like many key species, help keep ecosystems balanced — yet they also pose risks to humans.
Monitoring them is difficult due to their elusive behavior, and traditional fieldwork methods such as track counting, audio-trap reviews or DNA analysis require enormous time and labor.
The new AI method relies on an Audio Spectrogram Transformer (AST) neural architecture. The algorithm works in two stages: first, it detects any animal sounds in long field recordings; then, like a seasoned zoologist, it isolates the distinctive howl of a wolf.
The performance is striking: stage one detects animal sounds with ~98.3% accuracy, while stage two identifies wolf howls with ~89.6% accuracy. The AI system processes gigabytes of audio in hours instead of days, freeing scientists to focus on ecological analysis rather than manual review.
From Cameras to Soundscapes
Five years ago, automated image recognition from camera traps was considered a breakthrough. Today, research is shifting toward the acoustic domain. Projects such as AST-SED for sound-event recognition and Active Bird2Vec for bird-voice analysis highlight the rise of ‘acoustic ecology.’
The Russian wolf project marks the country’s first successful use of the AST architecture for wildlife sound recognition — placing Russian research squarely in a global trend.
Understanding wolf populations, migration patterns and behavior is vital for ecosystem balance, responsible hunting management and reducing human–predator conflict. The early-stage nature of the project is reflected in its publication in the international journal Scientific Reports, but its global potential is clear. A demonstration app is already available on GitHub.
Scaling Up to Other Species
The model can be adapted to monitor other key species — lynx, brown bears, endangered birds. Integrating the system with IoT audio sensors could create a unified digital platform for real‑time wildlife acoustic monitoring.
Pilot deployments in Russian nature reserves are expected within the next one to two years. By 2030, a full-scale system combining field sound recording, AI processing and analytic dashboards could become a standard tool for biodiversity assessment.
