  Everyday sound recognition aims to infer types of sound events in audio
streams. While many works succeeded in training models with high performance in
a fully-supervised manner, they are still restricted to the demand of large
quantities of labelled data and the range of predefined classes. To overcome
these drawbacks, this work firstly curates a new database named FSD-FS for
multi-label few-shot audio classification. It then explores how to incorporate
audio taxonomy in few-shot learning. Specifically, this work proposes
label-dependent prototypical networks (LaD-protonet) to exploit parent-children
relationships between labels. Plus, it applies taxonomy-aware label smoothing
techniques to boost model performance. Experiments demonstrate that
LaD-protonet outperforms original prototypical networks as well as other
state-of-the-art methods. Moreover, its performance can be further boosted when
combined with taxonomy-aware label smoothing.
