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Ecosystem Monitoring using Deep Learning

Primary supervisor

Bernd Meyer

The project develops methods to use acoustic data for the identification of animals in the wild and in controlled settings. It is part of a broader effort to build AI-enabled methods to support biodiversity and sustainability research. The initial objective is to use deep learning techniques to perform acoustic species identification in real-time on low-cost sensing devices coupled to cloud-based backends. Ultimately, we are aiming to move to Edge-AI, ie. to shift the AI entirely onto small embedded devices so that these can work autonomously without relying on cloud-based backends. Beyond species classification and individual recognition, the project may investigate the possibility to use advanced machine learning for the identification of individual-level and group-level behaviours. Data fusion with visual information may be considered as an extension to extend the range of detection capabilities.   


Project funding

Project based scholarship

Learn more about minimum entry requirements.