Investigating Target Class Influence on Neural Network Compressibility for Energy-Autonomous Avian Monitoring

The paper explores how the number of bird species (target classes) affects the compressibility of neural networks for passive acoustic monitoring on microcontroller units (MCUs). By training and compressing models with varying class counts, the authors show that significant compression can be achieved with minimal performance loss. They also benchmark different hardware platforms and assess the feasibility of deploying energy‑autonomous monitoring devices.

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