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arXiv · 2102.01993

Monaural Speech Enhancement with Complex Convolutional Block Attention Module and Joint Time Frequency Losses

Shengkui Zhao·Trung Hieu Nguyen·Bin Ma
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Abstract

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 (https://github.com/modelscope/ClearerVoice-Studio).

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BibTeXRIS

Shengkui Zhao, Trung Hieu Nguyen, Bin Ma. 2021-02-03. Monaural Speech Enhancement with Complex Convolutional Block Attention Module and Joint Time Frequency Losses. https://arxiv.org/abs/2102.01993

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