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

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Mourning Dove

Max Confidence: 95%

Occurrences: 89

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Great Horned Owl

Max Confidence: 97%

Occurrences: 46

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California Towhee

Max Confidence: 100%

Occurrences: 4078

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