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This paper presents a smart Passive Acoustic Monitoring (PAM) system that embeds a 1D-CNN classifier directly onto an AudioMoth microcontroller for in-situ soundscape analysis. The optimized 1D-CNN classifies Scopoli Shearwater seabird calls with 91% accuracy while maintaining a small memory footprint (~10kB RAM) and fast inference time (20ms). By selectively recording data upon target species detection and logging continuous classification results, the system enhances the efficiency and scalability of bioacoustic monitoring.
Dramatically extend the battery life of bioacoustic sensors by embedding a highly accurate CNN classifier directly on a microcontroller, enabling selective recording of target species.
Passive Acoustic Monitoring (PAM) is an efficient and non-invasive method for surveying ecosystems at a reduced cost. Typically, autonomous recorders allow the acquisition of vast bioacoustic datasets which are then analyzed. However, power consumption and data storage are both scarce and limit the duration of acquisition campaigns. To address this issue, we propose a smart PAM system which allows the in-situ analysis of the soundscape by embedding a classifier directly onto an AudioMoth microcontroller. Specifically, we propose an optimized yet simple 1D Convolutional Neural Network (1D-CNN) to classify the raw audio. The model focuses on the specific call of Scopoli Shearwater seabirds (endangered species) and is trained on a real-world dataset with a classification accuracy of 91\% (balanced accuracy of 89\%). We also propose a process to optimize the model to fit the severe resource constraints of the AudioMoth, achieving a \~10kB RAM memory footprint and 20ms inference time. Finally, we present an open-source tutorial of our model optimization and export strategy which can be used for embedding models beyond the scope of our study. Our modified version of the AudioMoth firmware adds two functions: (F1) which selectively records data when the target species has been detected and (F2) which logs the continuous classification results in real time. This work intends to facilitate the conception of intelligent sensors, enhancing the efficiency and scalability of bioacoustic monitoring campaigns.
