Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Unsupervised Discriminative Learning of Sounds for Audio Event
  Classification   Recent progress in network-based audio event classification has shown the
benefit of pre-training models on visual data such as ImageNet. While this
process allows knowledge transfer across different domains, training a model on
large-scale visual datasets is time consuming. On several audio event
classification benchmarks, we show a fast and effective alternative that
pre-trains the model unsupervised, only on audio data and yet delivers on-par
performance with ImageNet pre-training. Furthermore, we show that our
discriminative audio learning can be used to transfer knowledge across audio
datasets and optionally include ImageNet pre-training.

7
The categories of this paper are: hep-th
34
The categories of this paper are: chem-ph
9
The categories of this paper are: nlin
26
The categories of this paper are: bayes-an
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The categories of this paper are: atom-ph
12
The categories of this paper are: nucl-th
31
The categories of this paper are: ao-sci
16
The categories of this paper are: stat
5
The categories of this paper are: gr-qc
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The categories of this paper are: funct-an
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The categories of this paper are: solv-int
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The categories of this paper are: quant-ph
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The categories of this paper are: comp-gas
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The categories of this paper are: physics
2
The categories of this paper are: math
15
The categories of this paper are: nucl-ex