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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.
Embedded Systems and Machine Learning Engineer, OWL Integrations
Owen O'Donnell is a Machine Learning and Embedded Systems Engineer at OWL integrations. He works with training ML models to deploy in remote locations that will be running on resource constrained electronics. This introduces challenges such as needing smaller sized models and having... Read More →
Taraqur Rahman is Chief Data Scientist and Co-Founder at OWL Integrations and Organizer/Co-Founder of Biased Outliers, where he leads applied machine learning and data science initiatives with real-world impact. He combines deep technical expertise in Python with practical deployment... Read More →
