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.