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

TL;DR

This paper explores how the number of target bird species affects neural network compressibility for energy-efficient, in-field avian monitoring using microcontrollers, demonstrating effective model compression with minimal accuracy loss.

Contribution

It introduces a method for training and compressing neural networks for bird species detection on low-power microcontrollers, considering the impact of target class quantity.

Findings

Significant model compression achieved with minimal performance loss

Benchmarking results show feasibility on energy-autonomous devices

Model size and accuracy are influenced by the number of target classes

Abstract

Biodiversity loss poses a significant threat to humanity, making wildlife monitoring essential for assessing ecosystem health. Avian species are ideal subjects for this due to their popularity and the ease of identifying them through their distinctive songs. Traditionalavian monitoring methods require manual counting and are therefore costly and inefficient. In passive acoustic monitoring, soundscapes are recorded over long periods of time. The recordings are analyzed to identify bird species afterwards. Machine learning methods have greatly expedited this process in a wide range of species and environments, however, existing solutions require complex models and substantial computational resources. Instead, we propose running machine learning models on inexpensive microcontroller units (MCUs) directly in the field. Due to the resulting hardware and energy constraints, efficient artificial intelligence (AI) architecture is required. In this paper, we present our method for avian monitoring on MCUs. We trained and compressed models for various numbers of target classes to assess the detection of multiple bird species on edge devices and evaluate the influence of the number of species on the compressibility of neural networks. Our results demonstrate significant compression rates with minimal performance loss. We also provide benchmarking results for different hardware platforms and evaluate the feasibility of deploying energy-autonomous devices.