BirdNET is an artificial neural network from Cornell that matches vocalizations to identify bird species.
The program runs locally on a Raspberry Pi 5 server and analyzes audio segment in 6 second chunks, 24/7. The model currently being used is Global V2.4 with a minimum capture confidence of 0.70 and a sigmoid sensitivity of 1.25. Below are a few hand chosen examples with easy to see spectrographs.
Red Shouldered Hawk
Max Confidence: 99%
Occurrences: 550


Mourning Dove
Max Confidence: 95%
Occurrences: 89
Great Horned Owl
Max Confidence: 97%
Occurrences: 46


California Towhee
Max Confidence: 100%
Occurrences: 4078