Although supervised deep learning has revolutionized speech and audio processing, it has
necessitated the building of specialist models for individual tasks and application scenarios …

As biodiversity decreases worldwide, the development of effective techniques to track
changes in ecological communities becomes an urgent challenge. Together with other …

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

The popularity of machine learning (ML), deep learning (DL) and artificial intelligence (AI)
has risen sharply in recent years. Despite this spike in popularity, the inner workings of ML …

Inexpensive and accessible sensors are accelerating data acquisition in animal ecology.
These technologies hold great potential for large-scale ecological understanding, but are …

Distribution shifts—where the training distribution differs from the test distribution—can
substantially degrade the accuracy of machine learning (ML) systems deployed in the wild …

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

We introduce COLA, a self-supervised pre-training approach for learning a general-purpose
representation of audio. Our approach is based on contrastive learning: it learns a …

The range of technologies currently used in biodiversity conservation is staggering, with
innovative uses often adopted from other disciplines and being trialed in the field. We …

Deep learning is driving recent advances behind many everyday technologies, including
speech and image recognition, natural language processing and autonomous driving. It is …