  This work investigates pretrained audio representations for few shot Sound
Event Detection. We specifically address the task of few shot detection of
novel acoustic sequences, or sound events with semantically meaningful temporal
structure, without assuming access to non-target audio. We develop procedures
for pretraining suitable representations, and methods which transfer them to
our few shot learning scenario. Our experiments evaluate the general purpose
utility of our pretrained representations on AudioSet, and the utility of
proposed few shot methods via tasks constructed from real-world acoustic
sequences. Our pretrained embeddings are suitable to the proposed task, and
enable multiple aspects of our few shot framework.
