Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Audio-Based Music Classification with DenseNet And Data Augmentation   In recent years, deep learning technique has received intense attention owing
to its great success in image recognition. A tendency of adaption of deep
learning in various information processing fields has formed, including music
information retrieval (MIR). In this paper, we conduct a comprehensive study on
music audio classification with improved convolutional neural networks (CNNs).
To the best of our knowledge, this the first work to apply Densely Connected
Convolutional Networks (DenseNet) to music audio tagging, which has been
demonstrated to perform better than Residual neural network (ResNet).
Additionally, two specific data augmentation approaches of time overlapping and
pitch shifting have been proposed to address the deficiency of labelled data in
the MIR. Moreover, an ensemble learning of stacking is employed based on SVM.
We believe that the proposed combination of strong representation of DenseNet
and data augmentation can be adapted to other audio processing tasks.

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The categories of this paper are: hep-ex
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The categories of this paper are: adap-org
37
The categories of this paper are: supr-con
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The categories of this paper are: hep-lat
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The categories of this paper are: acc-phys
16
The categories of this paper are: stat
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The categories of this paper are: quant-ph
22
The categories of this paper are: chao-dyn
0
The categories of this paper are: hep-ph
32
The categories of this paper are: atom-ph
33
The categories of this paper are: plasm-ph
24
The categories of this paper are: dg-ga
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The categories of this paper are: q-bio
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The categories of this paper are: alg-geom
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The categories of this paper are: econ
17
The categories of this paper are: q-fin