About BirdNET
BirdNET is a research collaboration using machine learning to monitor global biodiversity. We develop open-source tools that transform bioacoustics from a specialized field into a scalable solution for conservation.
Science-led conservation.
BirdNET aims to lower the barrier to using sound for biodiversity monitoring. By combining deep learning with open tools and citizen science, we help track species and support conservation decisions at local to global scales.
BirdNET is a joint effort between:
- Cornell K. Lisa Yang Center for Conservation Bioacoustics, Cornell Lab of Ornithology
- Chair of Media Informatics, Chemnitz University of Technology
Supported by researchers, engineers, educators, and community contributors.
Acoustic Monitoring
Many species are more easily detected by sound than by sight. Passive monitoring captures activity without human disturbance across remote habitats and seasons.
Machine Learning
Manual review of audio is not feasible. AI automates species identification at scale, extracting features from noisy soundscapes with consistent precision.
Open Ecosystem
BirdNET provides core models for edge devices and reusable embeddings, supporting a global community of researchers and conservationists.
From soundscapes to insights
The BirdNET model is trained on thousands of hours of curated animal vocalizations. It is optimized to be robust against background noise while remaining efficient enough for real-time mobile use.
Design Priorities
- Noise robustness
- Real-time & Batch optimization
- High species coverage (11k+)
- Continuous data-driven updates
Audio is sampled at 32 kHz. The model analyzes short snippets of a few seconds, matching the typical duration of animal vocalizations.
Inside the model, the signal is converted into a spectrogram spanning 0 to 16 kHz, revealing the frequency patterns of each call in fine detail.
A Convolutional Neural Network (CNN) scans these visuals, utilizing millions of trained weights to detect species-specific patterns.
Initial predictions are cross-referenced with local metadata (location and date) to produce high-confidence species probabilities.
Supported by a global network
Institutional Support
Work at the Cornell K. Lisa Yang Center for Conservation Bioacoustics is made possible by the generosity of K. Lisa Yang, supporting innovative conservation technologies that scale global biodiversity monitoring.
Project Funding
- German Federal Ministry of Research, Technology and Space (FKZ 01|S22072)
- German Federal Ministry for the Environment (FKZ 67KI31040E)
- German Federal Ministry of Economic Affairs and Energy (FKZ 16KN095550)
- Deutsche Bundesstiftung Umwelt (39263/01)
- European Social Fund
Research Collaboration
BirdNET is a joint effort of partners from academia and industry. This multidisciplinary collaboration enables us to bridge the gap between AI research and practical field ecology.
Representative partner logos. See publications and tools for additional collaborators.