Burooj Ghani

My interests lie at the intersection of sound-based machine learning and ecology to inform the conservation of biodiversity. Bringing the power of Artificial Intelligence and bioacoustics to biodiversity monitoring. I completed my master’s and PhD in computer science from the University of Göttingen. As a graduate student, I worked among other things on efficient bird species classification, examining the impact of species selection on classification performance, and algorithmically analyzing intra-species song variations.

By harnessing machine learning techniques, I aim to develop innovative methods for detecting and classifying animal vocalisations. This has the potential to greatly benefit wildlife experts, conservation biologists and ecologists by providing automated tools for long-term environmental monitoring. The central focus of my work lies in processing extensive datasets of environmental sounds, enabling the extraction of valuable insights into the hidden world of animal communication. In nature, while many animals are visually elusive, their vocalizations offer a wealth of information about their habitats, seasonal changes, and interactions. Traditional methods of analyzing these sounds have been time-consuming and laborious. My goal is to create automated processes that accelerate and streamline the analysis, ultimately contributing to the protection and conservation of our planet’s diverse species and ecosystems.