Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: DF-Conformer: Integrated architecture of Conv-TasNet and Conformer using
  linear complexity self-attention for speech enhancement   Single-channel speech enhancement (SE) is an important task in speech
processing. A widely used framework combines an analysis/synthesis filterbank
with a mask prediction network, such as the Conv-TasNet architecture. In such
systems, the denoising performance and computational efficiency are mainly
affected by the structure of the mask prediction network. In this study, we aim
to improve the sequential modeling ability of Conv-TasNet architectures by
integrating Conformer layers into a new mask prediction network. To make the
model computationally feasible, we extend the Conformer using linear complexity
attention and stacked 1-D dilated depthwise convolution layers. We trained the
model on 3,396 hours of noisy speech data, and show that (i) the use of linear
complexity attention avoids high computational complexity, and (ii) our model
achieves higher scale-invariant signal-to-noise ratio than the improved
time-dilated convolution network (TDCN++), an extended version of Conv-TasNet.

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The categories of this paper are: econ
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The categories of this paper are: patt-sol
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The categories of this paper are: solv-int
0
The categories of this paper are: hep-ph
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The categories of this paper are: nucl-th
9
The categories of this paper are: nlin
27
The categories of this paper are: comp-gas
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The categories of this paper are: atom-ph
34
The categories of this paper are: chem-ph
8
The categories of this paper are: hep-ex
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The categories of this paper are: alg-geom
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The categories of this paper are: adap-org
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The categories of this paper are: physics
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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-alg