This thesis aims to investigate the effect of selecting different layers of various embedding models for transfer learning in passive acoustic monitoring. Specifically, it will explore the correlation between the performance of the embeddings and the proximity of the selected layer to the output layer, as well as the relatedness between the model’s domain and the target domain of passive acoustic monitoring.

Contact: Hannes Kath

Relevant Literature:

Leveraging transfer learning and active learning for data annotation in passive acoustic monitoring of wildlife (https://www.sciencedirect.com/science/article/pii/S1574954124002528?via%3Dihub)

Global birdsong embeddings enable superior transfer learning for bioacoustic classification (https://www.nature.com/articles/s41598-023-49989-z)