Acoustic Detection, Source Separation, and Classification Algorithms for Unmanned Aerial Vehicles in Wildlife Monitoring and Poaching

This work focuses on the problem of acoustic detection, source separation, and classification under noisy conditions. The goal of this work is to develop a system that is able to detect poachers and animals in the wild by using microphones mounted on unmanned aerial vehicles (UAVs). The classes of signals used to detect wildlife and poachers include: mammals, birds, vehicles and firearms. The noise signals under consideration include: colored noises, UAV propeller and wind noises. The system consists of three sub-systems: source separation (SS), signal detection, and signal classification. Non-negative Matrix Factorization (NMF) is used for source separation, and random forest classifiers are used for detection and classification. The source separation algorithm performance was evaluated using Signal to Distortion Ratio (SDR) for multiple signal classes and noises. The detection and classification algorithms where evaluated for accuracy of detection and classification for multiple signal classes and noises. The performance of the sub-systems and system as a whole are presented and discussed