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

2026-02-19

AI Summary

This study addresses the challenge of deploying bird acoustic monitoring on resource-constrained edge devices, where traditional approaches relying on manual annotation or complex models are impractical. Focusing on low-cost microcontroller units (MCUs), the work presents the first systematic investigation into how the number of target bird species affects the compressibility of neural networks. By integrating model training with compression techniques, the authors achieve efficient deployment and energy-efficiency evaluation across multiple MCU platforms. Experimental results demonstrate that the compressed models maintain high classification accuracy while achieving significant size reduction, thereby validating the feasibility of energy-autonomous, edge-based bird monitoring systems. This advancement facilitates the practical application of lightweight AI in ecological monitoring scenarios.

Technology Category

• Machine Learning: Learning on the Edge & Model Compression
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Humans and AI: Crowd Sourcing and Human Computation

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

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.

Problem

• neural network compressibility
• energy-autonomous monitoring
• avian species detection
• edge AI
• microcontroller deployment

Innovation

• neural network compression
• edge AI
• microcontroller unit (MCU)
• passive acoustic monitoring
• energy-autonomous sensing