ID birds by sound. How can computers learn to recognize birds from sounds? The K. Our research is mainly focused on the detection and classification of avian sounds using machine learning β we want to assist experts and citizen scientist in their work of monitoring and protecting our birds. BirdNET is a research platform that aims at recognizing birds by sound at scale. We support various hardware and operating systems such as Arduino microcontrollers, the Raspberry Pi, smartphones, web browsers, workstation PCs, and even cloud services.
BirdNET is a citizen science platform as well as an analysis software for extremely large collections of audio. BirdNET aims to provide innovative tools for conservationists, biologists, and birders alike. This page features some of our public demonstrations, including a live stream demo , a demo for the analysis of audio recordings , an Android and iOS app , and its visualization of submissions. All demos are based on an artificial neural network we call BirdNET.
We are constantly improving the features and performance of our demos β please make sure to check back with us regularly. We will add more species in the near future.
Advances in machine learning are making it easier to identify birds by their sounds. The live stream demo processes a live audio stream from a microphone outside the Cornell Lab of Ornithology, located in the Sapsucker Woods sanctuary in Ithaca, New York. This demo features an artificial neural network trained on the most common species of the Sapsucker Woods area. Our system splits the audio stream into segments, converts those segments into spectrograms visual representations of the audio signal and passes the spectrograms through a convolutional neural network, all in near-real-time.
The web page accumulates the species probabilities of the last five seconds into one prediction. This demo is intended for large screens. Follow this link to view the demo. Reliable identification of bird species in recorded audio files would be a transformative tool for researchers, conservation biologists, and birders. This demo provides a web interface for the upload and analysis of audio recordings. Based on an artificial neural network featuring almost 1, of the most common species of North America and Europe, this demo shows the most probable species for every second of the recording.
