Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Deep Learning of Human Perception in Audio Event Classification   In this paper, we introduce our recent studies on human perception in audio
event classification by different deep learning models. In particular, the
pre-trained model VGGish is used as feature extractor to process audio data,
and DenseNet is trained by and used as feature extractor for our
electroencephalography (EEG) data. The correlation between audio stimuli and
EEG is learned in a shared space. In the experiments, we record brain
activities (EEG signals) of several subjects while they are listening to music
events of 8 audio categories selected from Google AudioSet, using a 16-channel
EEG headset with active electrodes. Our experimental results demonstrate that
i) audio event classification can be improved by exploiting the power of human
perception, and ii) the correlation between audio stimuli and EEG can be
learned to complement audio event understanding.

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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: math
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The categories of this paper are: acc-phys
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The categories of this paper are: math-ph
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The categories of this paper are: nucl-ex
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The categories of this paper are: econ
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The categories of this paper are: astro-ph
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The categories of this paper are: chem-ph
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The categories of this paper are: cmp-lg
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The categories of this paper are: plasm-ph
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The categories of this paper are: funct-an
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The categories of this paper are: q-fin
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The categories of this paper are: ao-sci
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The categories of this paper are: cond-mat
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The categories of this paper are: physics