Automatic detection and classification of animal sounds has many applications in
biodiversity monitoring and animal behavior. In the past twenty years, the volume of digitised …

Few-shot sound event detection is the task of detecting sound events, despite having only a
few labelled examples of the class of interest. This framework is particularly useful in …

Few-shot bioacoustic event detection is a task that detects the occurrence time of a novel
sound given a few examples. Previous methods employ metric learning to build a latent …

New classes of sounds constantly emerge with a few samples, making it challenging for
models to adapt to dynamic acoustic environments. This challenge motivates us to address …

Continuously learning new classes without catastrophic forgetting is a challenging problem
for on-device environmental sound classification given the restrictions on computation …

Bioacoustic sound event detection allows for better understanding of animal behavior and
for better monitoring biodiversity using audio. Deep learning systems can help achieve this …

Sound event detection involves the identification and temporal localization of sound events
within audio recordings. Bioacoustic sound event detection specifically targets animal …

In this paper we study two major challenges in few-shot bioacoustic event detection: variable
event lengths and false-positives. We use prototypical networks where the embedding …

Detecting the presence of animal vocalisations in nature is essential to study animal
populations and their behaviors. A recent development in the field is the introduction of the …

In few-shot bioacoustic event detection, besides interested target events, background noises
and various uninterested sound events lead to complex decision boundaries, which require …