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[2602.17751] Investigating Target Class Influence on Neural Network Compressibility for Energy-Autonomous Avian Monitoring
Summary
This paper explores the impact of target class selection on the compressibility of neural networks for avian monitoring using energy-autonomous devices, demonstrating efficient AI architecture for field applications.
Why It Matters
As biodiversity loss becomes a pressing global issue, effective wildlife monitoring is crucial. This research presents a novel approach to deploying machine learning on low-power devices, enhancing the ability to monitor bird populations efficiently and sustainably.
Key Takeaways
- The study assesses how the number of target bird species affects neural network compressibility.
- Efficient AI models can be deployed on inexpensive microcontroller units in the field.
- Significant compression rates were achieved with minimal performance loss, making it feasible for energy-autonomous monitoring.
Computer Science > Machine Learning arXiv:2602.17751 (cs) [Submitted on 19 Feb 2026] Title:Investigating Target Class Influence on Neural Network Compressibility for Energy-Autonomous Avian Monitoring Authors:Nina Brolich, Simon Geis, Maximilian Kasper, Alexander Barnhill, Axel Plinge, Dominik Seuß View a PDF of the paper titled Investigating Target Class Influence on Neural Network Compressibility for Energy-Autonomous Avian Monitoring, by Nina Brolich and 5 other authors View PDF HTML (experimental) 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 metho...
