Bioacoustic research provides invaluable insights into the behavior, ecology, and
conservation of animals. Most bioacoustic datasets consist of long recordings where events …

We propose a shift towards end-to-end learning in bird sound monitoring by combining self-
supervised (SSL) and deep active learning (DAL). Leveraging transformer models, we aim …

Within the recent deep learning revolution, transformer architectures and pretrained self-
supervised models opened up many perspectives for the study of linguistics and animal …

The lack of annotated training data in bioacoustics hinders the use of large-scale neural
network models trained in a supervised way. In order to leverage a large amount of …

This study investigates the potential of automated deep learning to enhance the accuracy
and efficiency of multi-class classification of bird vocalizations, compared against traditional …

Passive acoustic monitoring offers a scalable, non-invasive method for tracking global
biodiversity and anthropogenic impacts on species. Although deep learning has become a …

Animal vocalisations and natural soundscapes are fascinating objects of study, and contain
valuable evidence about animal behaviours, populations and ecosystems. They are studied …

Deep learning (DL) models have emerged as a powerful tool in avian bioacoustics to
diagnose environmental health and biodiversity. However, inconsistencies in research pose …

Automated bioacoustic analysis aids understanding and protection of both marine and
terrestrial animals and their habitats across extensive spatiotemporal scales, and typically …

Monitoring of bird populations has played a vital role in conservation efforts and in
understanding biodiversity loss. The automation of this process has been facilitated by both …