BirdNET-Analyzer

AI-powered engine for large-scale bioacoustic monitoring: processes thousands of hours of audio to identify over 6,000 bird species.

BirdNET-Analyzer is the core scientific workflow tool for automated bioacoustic monitoring, developed by the Cornell Lab of Ornithology and Chemnitz University of Technology. It applies advanced BirdNET deep learning models to large audio datasets, efficiently processing thousands of hours of recordings from passive acoustic monitoring (PAM) arrays. The V2.4 model identifies over 6,000 global bird species, providing researchers with specific outputs: timestamps, species labels, and confidence scores. This capability scales biodiversity tracking from pilot studies to multi-year deployments, supporting both CLI and GUI workflows.

https://birdnet.cornell.edu