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.
Machine learning, deep learning, AI, audio, bioacoustics
I currently work within the realm of three EU-funded projects MAMBO, GUARDEN and TETTRIs to advance biodiversity conservation by developing machine learning methods for detecting and classifying animal vocalisations.
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.
- Ghani, Burooj, et al. Global birdsong embeddings enable superior transfer learning for bioacoustic classification, Nature Scientific Reports (2023)
- Nolasco, Ines, ... , Ghani, Burooj, et al. "Learning to learn an animal sound from five examples." Ecological Informatics (2023)
- Ghani, Burooj et al. “Classification of group-specific variations in songs within House Wren species using machine learning models.” Ecol. Informatics 74 (2022): 101946.
- Ghani, Burooj and Sarah Hallerberg. “A Randomized Bag-of-Birds Approach to Study Robustness of Automated Audio Based Bird Species Classification.” Applied Sciences (2021)