BirdNET

BirdNET is a research collaboration that uses machine learning to recognize birds by sound and make acoustic monitoring accessible to everyone. It is a joint effort between the K. Lisa Yang Center for Conservation Bioacoustics at the Cornell Lab of Ornithology and the Chair of Media Informatics at Chemnitz University of Technology. BirdNET aims to lower the barrier to using sound for biodiversity monitoring by combining deep learning with open tools and citizen science. The platform provides models and open tools that turn audio into species presence information. It is used in backyard stations, migration studies, protected area assessments, and citizen science challenges. The core models power apps and edge devices, and the embeddings are reused for new classification tasks. The workflow involves capturing audio via microphones or recorders, preprocessing it into spectrograms, and using an artificial neural network to generate species confidence scores. The app recognizes thousands of animal species from their vocalizations. It supports various hardware including Arduino microcontrollers, Raspberry Pi, smartphones, and web browsers.