Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Monaural Speech Enhancement with Complex Convolutional Block Attention
  Module and Joint Time Frequency Losses   Deep complex U-Net structure and convolutional recurrent network (CRN)
structure achieve state-of-the-art performance for monaural speech enhancement.
Both deep complex U-Net and CRN are encoder and decoder structures with skip
connections, which heavily rely on the representation power of the
complex-valued convolutional layers. In this paper, we propose a complex
convolutional block attention module (CCBAM) to boost the representation power
of the complex-valued convolutional layers by constructing more informative
features. The CCBAM is a lightweight and general module which can be easily
integrated into any complex-valued convolutional layers. We integrate CCBAM
with the deep complex U-Net and CRN to enhance their performance for speech
enhancement. We further propose a mixed loss function to jointly optimize the
complex models in both time-frequency (TF) domain and time domain. By
integrating CCBAM and the mixed loss, we form a new end-to-end (E2E) complex
speech enhancement framework. Ablation experiments and objective evaluations
show the superior performance of the proposed approaches.

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The categories of this paper are: physics
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The categories of this paper are: mtrl-th
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The categories of this paper are: cond-mat
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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: astro-ph
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The categories of this paper are: hep-lat
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The categories of this paper are: atom-ph
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The categories of this paper are: acc-phys
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The categories of this paper are: hep-th
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The categories of this paper are: hep-ex
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The categories of this paper are: comp-gas
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The categories of this paper are: nucl-th
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The categories of this paper are: adap-org
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The categories of this paper are: hep-ph
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The categories of this paper are: math