AI model embedded on microcontroller enables smart bioacoustic monitoring

Researchers have developed a smart passive acoustic monitoring system that embeds a classifier directly onto an AudioMoth microcontroller to analyze soundscapes in situ. This system utilizes an optimized 1D Convolutional Neural Network to classify the specific calls of endangered Scopoli Shearwater seabirds with 91% accuracy. The model is designed to fit the microcontroller's resource constraints, requiring approximately 10kB of RAM and achieving a 20ms inference time, thereby enhancing the efficiency and scalability of bioacoustic monitoring. AI

IMPACT This work facilitates the creation of intelligent sensors for more efficient and scalable bioacoustic monitoring campaigns.

RANK_REASON This is a research paper detailing a novel application of a 1D CNN for bioacoustic monitoring. [lever_c_demoted from research: ic=1 ai=1.0]

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• Scopoli Shearwater
• 1D Convolutional Neural Network
• AudioMoth

• paper
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