  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 bioacoustics, where often there is a need to annotate
very long recordings but the expert annotator time is limited. This paper
presents an overview of the second edition of the few-shot bioacoustic sound
event detection task included in the DCASE 2022 challenge. A detailed
description of the task objectives, dataset, and baselines is presented,
together with the main results obtained and characteristics of the submitted
systems. This task received submissions from 15 different teams from which 13
scored higher than the baselines. The highest F-score was of 60% on the
evaluation set, which leads to a huge improvement over last year's edition.
Highly-performing methods made use of prototypical networks, transductive
learning, and addressed the variable length of events from all target classes.
Furthermore, by analysing results on each of the subsets we can identify the
main difficulties that the systems face, and conclude that few-show bioacoustic
sound event detection remains an open challenge.
