Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Automatic tagging using deep convolutional neural networks   We present a content-based automatic music tagging algorithm using fully
convolutional neural networks (FCNs). We evaluate different architectures
consisting of 2D convolutional layers and subsampling layers only. In the
experiments, we measure the AUC-ROC scores of the architectures with different
complexities and input types using the MagnaTagATune dataset, where a 4-layer
architecture shows state-of-the-art performance with mel-spectrogram input.
Furthermore, we evaluated the performances of the architectures with varying
the number of layers on a larger dataset (Million Song Dataset), and found that
deeper models outperformed the 4-layer architecture. The experiments show that
mel-spectrogram is an effective time-frequency representation for automatic
tagging and that more complex models benefit from more training data.

16
The categories of this paper are: stat
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The categories of this paper are: eess
17
The categories of this paper are: q-fin
13
The categories of this paper are: quant-ph
5
The categories of this paper are: gr-qc
36
The categories of this paper are: cmp-lg
7
The categories of this paper are: hep-th
30
The categories of this paper are: q-alg
10
The categories of this paper are: q-bio
18
The categories of this paper are: econ
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The categories of this paper are: nlin
14
The categories of this paper are: math-ph
6
The categories of this paper are: astro-ph
32
The categories of this paper are: atom-ph
25
The categories of this paper are: patt-sol
23
The categories of this paper are: solv-int