Bird@Edge: Bird Species Recognition at the Edge

We’ve created Bird@Edge, an Edge AI system for recognizing bird species in audio recordings to support real-time biodiversity monitoring. Bird@Edge is based on embedded edge devices operating in a distributed system to enable efficient, continuous evaluation of soundscapes recorded in forests.

There are multiple ESP32-based microphones (called Bird@Edge Mics) stream audio to a local Bird@Edge Station, on which bird species recognition is performed. The results of several Bird@Edge Stations are transmitted to a backend cloud for further analysis, e.g., by biodiversity researchers.

To recognize bird species in soundscapes, a deep neural network based on the EfficientNet-B3 architecture is trained and optimized for execution on embedded edge devices and deployed on a NVIDIA Jetson Nano board using the DeepStream SDK.

We have some ressources available online if you are interested:

Down below are some pictures of the devices we’ve build for outdoor usage.

We’d be happy if you’d try our systems yourself!
Jonas


Bird@Edge Mic including solar panel (left), Bird@Edge Station including battery box (right).


Close-up of the Bird@Edge Mic’s internals.

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