Who Calls the Shots Rethinking Few-Shot Learning for Audio

Document Type

Conference Proceeding

Publication Date

1-1-2021

Abstract

Few-shot learning aims to train models that can recognize novel classes given just a handful of labeled examples, known as the support set. While the field has seen notable advances in recent years, they have often focused on multi-class image classification. Audio, in contrast, is often multi-label due to overlapping sounds, resulting in unique properties such as polyphony and signal-to-noise ratios (SNR). This leads to unanswered questions concerning the impact such audio properties may have on few-shot learning system design, performance, and human-computer interaction, as it is typically up to the user to collect and provide inference-time support set examples. We address these questions through a series of experiments designed to elucidate the answers to these questions. We introduce two novel datasets, FSD-MIX-CLIPS and FSD-MIX-SED, whose programmatic generation allows us to explore these questions systematically. Our experiments lead to audio-specific insights on few-shot learning, some of which are at odds with recent findings in the image domain: there is no best one-size- fits-all model, method, and support set selection criterion. Rather, it depends on the expected application scenario. Our code and data are available at https://github.com/wangyu/rethink-audio-fsl.

Identifier

85123418497 (Scopus)

ISBN

[9781665448703]

Publication Title

IEEE Workshop on Applications of Signal Processing to Audio and Acoustics

External Full Text Location

https://doi.org/10.1109/WASPAA52581.2021.9632677

e-ISSN

19471629

ISSN

19311168

First Page

36

Last Page

40

Volume

2021-October

Grant

1544753

Fund Ref

National Science Foundation

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