Lightning Talk: Deep Learning in the Wild: Embedded PyTorch for Real-World Conservation Bioacoustics - Taraqur Rahman & Owen O'Donnell, OWL Integrations
Passive acoustic monitoring is a powerful tool for wildlife conservation, but deploying deep learning models in remote rainforest environments introduces strict constraints on power, memory, and compute. In this talk, we present an end-to-end PyTorch-based pipeline for detecting and analyzing the endangered three-wattled bellbird using embedded deep learning systems.
We cover the full lifecycle from audio preprocessing and model training in PyTorch to optimization and deployment on resource-constrained embedded devices. Topics include model architectures for sparse bioacoustic event detection, handling extreme class imbalance, model compression and quantization, and practical trade-offs between accuracy, latency, and power consumption.
The session emphasizes real-world lessons learned deploying machine learning at the edge, where unreliable connectivity, noisy signals, and limited hardware define success more than benchmark metrics. Attendees will gain practical patterns for building and deploying PyTorch models for embedded and edge AI applications with real environmental impact.