Raphael Weber

• African nature reserves face significant challenges in monitoring wildlife and detecting threats such as poaching. Traditional monitoring methods, such as patrols or camera traps, are often insufficient due to the vast and remote areas that need to be covered. This thesis proposes an acoustic monitoring system designed to address these challenges. The system leverages advanced signal processing and machine learning algorithms to analyze acoustic data in near real-time, enabling the detection of wildlife, human activity, and other environmental sounds. This research presents the design and possible implementation of the proposed system. The system has not been built, deployed, or tested. However, the results of this research indicate that such a system is capable of accurately detecting and classifying acoustic events of interest. The thesis also discusses various challenges and considerations for deploying the system in African nature reserves, such as terrain and environmental factors, legal considerations, cultural and social factors, and technical and operational considerations. Recommendations for addressing these challenges and potential directions for future research are also provided. Ultimately, this research contributes to the ongoing efforts to develop innovative technologies for wildlife conservation and demonstrates the potential of acoustic monitoring as a valuable tool for protecting African nature reserves.