  Many applications involve detecting and localizing specific sound events
within long, untrimmed documents, including keyword spotting, medical
observation, and bioacoustic monitoring for conservation. Deep learning
techniques often set the state-of-the-art for these tasks. However, for some
types of events, there is insufficient labeled data to train such models. In
this paper, we propose a region proposal-based approach to few-shot sound event
detection utilizing the Perceiver architecture. Motivated by a lack of suitable
benchmark datasets, we generate two new few-shot sound event localization
datasets: "Vox-CASE," using clips of celebrity speech as the sound event, and
"ESC-CASE," using environmental sound events. Our highest performing proposed
few-shot approaches achieve 0.483 and 0.418 F1-score, respectively, with 5-shot
5-way tasks on these two datasets. These represent relative improvements of
72.5% and 11.2% over strong proposal-free few-shot sound event detection
baselines.
