Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Concatenated Identical DNN (CI-DNN) to Reduce Noise-Type Dependence in
  DNN-Based Speech Enhancement   Estimating time-frequency domain masks for speech enhancement using deep
learning approaches has recently become a popular field of research. In this
paper, we propose a mask-based speech enhancement framework by using
concatenated identical deep neural networks (CI-DNNs). The idea is that a
single DNN is trained under multiple input and output signal-to-noise power
ratio (SNR) conditions, using targets that provide a moderate SNR gain with
respect to the input and therefore achieve a balance between speech component
quality and noise suppression. We concatenate this single DNN several times
without any retraining to provide enough noise attenuation. Simulation results
show that our proposed CI-DNN outperforms enhancement methods using classical
spectral weighting rules w.r.t. total speech quality and speech
intelligibility. Moreover, our approach shows similar or even a little bit
better performance with much fewer trainable parameters compared with a
noisy-target single DNN approach of the same size. A comparison to the
conventional clean-target single DNN approach shows that our proposed CI-DNN is
better in speech component quality and much better in residual noise component
quality. Most importantly, our new CI-DNN generalized best to an unseen noise
type, if compared to the other tested deep learning approaches.

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The categories of this paper are: nucl-th
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The categories of this paper are: nucl-ex
30
The categories of this paper are: q-alg
6
The categories of this paper are: astro-ph
20
The categories of this paper are: acc-phys
27
The categories of this paper are: comp-gas
33
The categories of this paper are: plasm-ph
22
The categories of this paper are: chao-dyn
5
The categories of this paper are: gr-qc
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The categories of this paper are: nlin
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The categories of this paper are: cond-mat
1
The categories of this paper are: cs
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The categories of this paper are: ao-sci
34
The categories of this paper are: chem-ph
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The categories of this paper are: mtrl-th
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The categories of this paper are: q-fin