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Fullsubnet: A Full-band And Sub-band Fusion Model For Real-time Single-channel Speech Enhancement

2020 · Xiang Hao, Xiangdong Su, Radu Horaud, et al.

Abstract

This paper proposes a full-band and sub-band fusion model, named as FullSubNet, for single-channel real-time speech enhancement. Full-band and sub-band refer to the models that input full-band and sub-band noisy spectral feature, output full-band and sub-band speech target, respectively. The sub-band model processes each frequency independently. Its input consists of one frequency and several context frequencies. The output is the prediction of the clean speech target for the corresponding frequency. These two types of models have distinct characteristics. The full-band model can capture the global spectral context and the long-distance cross-band dependencies. However, it lacks the ability to modeling signal stationarity and attending the local spectral pattern. The sub-band model is just the opposite. In our proposed FullSubNet, we connect a pure full-band model and a pure sub-band model sequentially and use practical joint training to integrate these two types of models' advantages.

Authors

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Tags

  • Speech Enhancement

Stats

  • citations189
  • S2 citations—
  • github stars0
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  • heat score17.09
  • arxiv keyhao2020fullsubnet

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