In the context of passive acoustic monitoring (PAM) better models are needed to reliably
gain insights from large amounts of raw, unlabeled data. Bioacoustics foundation models …

Building accurate knowledge of the identity, the geographic distribution and the evolution of
species is essential for the sustainable development of humanity, as well as for biodiversity …

Bioacoustics has become widely used in the study of acoustically active animals, and
machine learning algorithms have emerged as efficient and effective strategies to identify …

There is a need for monitoring biodiversity at multiple spatial and temporal scales to aid
conservation efforts. Autonomous recording units (ARUs) can provide cost-effective, long …

The study of bird populations is crucial for biodiversity research and conservation. Deep
artificial neural networks have revolutionized bird acoustic recognition; however, most …

The ability for a machine learning model to cope with differences in training and deployment
conditions--eg in the presence of distribution shift or the generalization to new classes …

Multi‐rotor drones equipped with acoustic sensors have great potential for bioacoustically
monitoring vocal species in the environment for biodiversity conservation. The bottleneck of …

We present TaxaBind, a unified embedding space for characterizing any species of interest.
TaxaBind is a multimodal embedding space across six modalities: ground-level images of …

Monitoring of bird species plays a vital role in understanding biodiversity trends, as birds
serve as reliable indicators of ecological change. Traditional observer-based bird surveys …

It is easier to hear birds than see them. However, they still play an essential role in nature
and are excellent indicators of deteriorating environmental quality and pollution. Recent …