Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Audio Dequantization for High Fidelity Audio Generation in Flow-based
  Neural Vocoder   In recent works, a flow-based neural vocoder has shown significant
improvement in real-time speech generation task. The sequence of invertible
flow operations allows the model to convert samples from simple distribution to
audio samples. However, training a continuous density model on discrete audio
data can degrade model performance due to the topological difference between
latent and actual distribution. To resolve this problem, we propose audio
dequantization methods in flow-based neural vocoder for high fidelity audio
generation. Data dequantization is a well-known method in image generation but
has not yet been studied in the audio domain. For this reason, we implement
various audio dequantization methods in flow-based neural vocoder and
investigate the effect on the generated audio. We conduct various objective
performance assessments and subjective evaluation to show that audio
dequantization can improve audio generation quality. From our experiments,
using audio dequantization produces waveform audio with better harmonic
structure and fewer digital artifacts.

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The categories of this paper are: hep-th
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The categories of this paper are: physics
33
The categories of this paper are: plasm-ph
32
The categories of this paper are: atom-ph
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The categories of this paper are: econ
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The categories of this paper are: dg-ga
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The categories of this paper are: supr-con
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The categories of this paper are: ao-sci
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The categories of this paper are: alg-geom
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The categories of this paper are: nucl-ex
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The categories of this paper are: stat
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The categories of this paper are: math-ph
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The categories of this paper are: comp-gas
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The categories of this paper are: acc-phys
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The categories of this paper are: astro-ph
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The categories of this paper are: chem-ph