Machine learning and acoustic monitoring for wildlife conservation
Summary
Research on automated analysis of years of audio data from natural habitats using machine learning. Focuses on few-shot learning for annotation-efficient methods and feature extraction for adapting time-frequency representations to acoustic models for wildlife sound detection.
Themes
Keywords
machine listening, wildlife conservation, few-shot learning, acoustic monitoring, sound event detection, bioacoustics
Poster
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