Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Randomly weighted CNNs for (music) audio classification   The computer vision literature shows that randomly weighted neural networks
perform reasonably as feature extractors. Following this idea, we study how
non-trained (randomly weighted) convolutional neural networks perform as
feature extractors for (music) audio classification tasks. We use features
extracted from the embeddings of deep architectures as input to a classifier -
with the goal to compare classification accuracies when using different
randomly weighted architectures. By following this methodology, we run a
comprehensive evaluation of the current deep architectures for audio
classification, and provide evidence that the architectures alone are an
important piece for resolving (music) audio problems using deep neural
networks.

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The categories of this paper are: plasm-ph
17
The categories of this paper are: q-fin
23
The categories of this paper are: solv-int
34
The categories of this paper are: chem-ph
16
The categories of this paper are: stat
30
The categories of this paper are: q-alg
9
The categories of this paper are: nlin
15
The categories of this paper are: nucl-ex
35
The categories of this paper are: mtrl-th
36
The categories of this paper are: cmp-lg
3
The categories of this paper are: physics
27
The categories of this paper are: comp-gas
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The categories of this paper are: math-ph
11
The categories of this paper are: hep-lat
2
The categories of this paper are: math
32
The categories of this paper are: atom-ph