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arXiv · 2212.08952

Learning from Taxonomy: Multi-label Few-Shot Classification for Everyday Sound Recognition

Jinhua Liang·Huy Phan·Emmanouil Benetos
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Abstract

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.

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Jinhua Liang, Huy Phan, Emmanouil Benetos. 2022-12-17. Learning from Taxonomy: Multi-label Few-Shot Classification for Everyday Sound Recognition. https://arxiv.org/abs/2212.08952

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