Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: MetaAudio: A Few-Shot Audio Classification Benchmark   Currently available benchmarks for few-shot learning (machine learning with
few training examples) are limited in the domains they cover, primarily
focusing on image classification. This work aims to alleviate this reliance on
image-based benchmarks by offering the first comprehensive, public and fully
reproducible audio based alternative, covering a variety of sound domains and
experimental settings. We compare the few-shot classification performance of a
variety of techniques on seven audio datasets (spanning environmental sounds to
human-speech). Extending this, we carry out in-depth analyses of joint training
(where all datasets are used during training) and cross-dataset adaptation
protocols, establishing the possibility of a generalised audio few-shot
classification algorithm. Our experimentation shows gradient-based
meta-learning methods such as MAML and Meta-Curvature consistently outperform
both metric and baseline methods. We also demonstrate that the joint training
routine helps overall generalisation for the environmental sound databases
included, as well as being a somewhat-effective method of tackling the
cross-dataset/domain setting.

26
The categories of this paper are: bayes-an
33
The categories of this paper are: plasm-ph
0
The categories of this paper are: hep-ph
37
The categories of this paper are: supr-con
15
The categories of this paper are: nucl-ex
4
The categories of this paper are: cond-mat
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The categories of this paper are: solv-int
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The categories of this paper are: acc-phys
14
The categories of this paper are: math-ph
3
The categories of this paper are: physics
10
The categories of this paper are: q-bio
8
The categories of this paper are: hep-ex
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The categories of this paper are: chao-dyn
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The categories of this paper are: adap-org
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The categories of this paper are: econ
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The categories of this paper are: dg-ga