BirdNET-Go
A modern interface for real-time bird sound detection and classification
BirdNET-Go is a Go-based implementation for real-time bird sound detection and classification, built upon the foundation of the BirdNET project. This application provides a user-friendly interface for continuous bird sound monitoring and analysis.
BirdNET-Go would not be possible without the groundbreaking work of the BirdNET project. AI model used in BirdNET-Go is work of BirdNET project and is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Developed by
The K. Lisa Yang Center for Conservation Bioacoustics at the Cornell Lab of Ornithology in collaboration with Chemnitz University of Technology:
- Stefan Kahl
- Connor Wood
- Maximilian Eibl
- Holger Klinck
BirdNET-Go has grown and improved thanks to the collaborative efforts of many individuals. Their contributions, ranging from code improvements to feature suggestions, have been invaluable in making this project better.
Main Developer
GitHub Contributors
Special thanks to these contributors for their valuable work on improving BirdNET-Go:
Community Acknowledgment
We would also like to extend our gratitude to all users who have contributed through:
- Bug reports and issue tracking
- Feature suggestions and feedback
- Testing and validation
- Documentation improvements
BirdNET-Pi Project
Special thanks to Patrick McGuire for his inspiring work on BirdNET-Pi, which has influenced the development of BirdNET-Go. BirdNET-Pi demonstrates the potential of bird sound detection on embedded devices and has set a high standard for community-driven development.
BirdNET Label Translations
Provided by Patrick Levin for the BirdNET-Pi project by Patrick McGuire
Version Information
Current Version: v0.6.3