Smart Passive Acoustic Monitoring: Embedding a Classifier on AudioMoth Microcontroller
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 ...
| Authors | Louis Lerbourg, Paul Peyret, Juliette Linossier et al. |
|---|---|
| Primary category | cs.SD |
| Published | 5 May 2026 |
| Last updated at source | 5 May 2026 |
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