A speech enhancement method based on depthwise separable convolution and MambaUNet

MENG Xiangcai; ZHUANG Yunpeng; ZHONG Qiang; OU Shifeng

doi:10.11959/j.issn.1000-0801.2025272

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A speech enhancement method based on depthwise separable convolution and MambaUNet

Research and Development | 更新时间：2026-01-08

- A speech enhancement method based on depthwise separable convolution and MambaUNet
- Telecommunications Science Vol. 41, Issue 12, Pages: 181-190(2025)
- 作者机构：
  
  1.烟台职业学院智能控制系，山东烟台 264035
  2.烟台弘武机电科技有限公司，山东烟台 264035
  3.烟台大学物理与电子信息学院，山东烟台 264005
- 作者简介：
- 基金信息：
  
  The Natural Science Foundation Youth Project of Shandong Province(ZR2024QB388);Yantai Vocational College School based Research Project(2024XBYB005)
- DOI：10.11959/j.issn.1000-0801.2025272
  CLC： TN912.3
- Received：30 May 2025，
  
  Revised：2025-10-11，
  
  Accepted：11 October 2025，
  
  Published：20 December 2025
- 稿件说明：
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孟祥彩,庄云朋,钟强等.基于深度可分离卷积的MambaUNet的语音增强方法[J].电信科学,2025,41(12):181-190.

MENG Xiangcai,ZHUANG Yunpeng,ZHONG Qiang,et al.A speech enhancement method based on depthwise separable convolution and MambaUNet[J].Telecommunications Science,2025,41(12):181-190.
孟祥彩,庄云朋,钟强等.基于深度可分离卷积的MambaUNet的语音增强方法[J].电信科学,2025,41(12):181-190. DOI： 10.11959/j.issn.1000-0801.2025272.

MENG Xiangcai,ZHUANG Yunpeng,ZHONG Qiang,et al.A speech enhancement method based on depthwise separable convolution and MambaUNet[J].Telecommunications Science,2025,41(12):181-190. DOI： 10.11959/j.issn.1000-0801.2025272.

摘要

致力于提升语音增强技术在复杂噪声环境中的鲁棒性与建模效率，提出了一种基于深度可分离卷积与结构化状态空间模型融合的语音增强网络（DW-MambaUNet）。该网络以U-Net结构为基础，引入TF-Mamba模块在时序与频率双路径上建模全局依赖，同时结合深度可分离卷积增强局部特征提取能力，从而实现对语音信号的多尺度特征恢复与精细增强。模型在频谱重建过程中通过可学习Sigmoid与Arctan2函数分别优化幅度与相位输出，在保持参数量较小的前提下大幅提升了语音质量。此外，引入动态权重调节策略，结合损失历史的平滑趋势与语音质量感知评估（perceptual evaluation of speech quality，PESQ）感知反馈机制，自适应平衡多任务损失函数的重要性，有效缓解固定加权方式导致的训练收敛瓶颈。在VoiceBank+DEMAND与TIMIT数据集上的实验结果表明，所提DW-MambaUNet在PESQ、STOI、MOS等多个指标上均优于现有多种主流语音增强模型，尤其在低信噪比条件下表现出良好的增强效果与泛化能力。消融实验进一步验证了TF-Mamba模块与DWConv结构对模型性能的贡献。该研究为低复杂度、高性能的语音增强模型设计提供了新思路，具有良好的理论意义与应用价值。

Abstract

Aiming to enhance the robustness and modeling efficiency of speech enhancement technology in complex noisy environments

a novel speech enhancement network

DW-MambaUNet was proposed

which integrated depthwise separable convolution and a structured state space model. Built upon a U-Net architecture

the TF-Mamba module was incorporated to model global dependencies along both temporal and frequency paths

while the depthwise separable convolution enhanced local feature extraction. Effective multi-scale feature restoration and fine-grained enhancement of speech signals were enabled by this design. During the spectrogram reconstruction process

learnable Sigmoid and Arctan2 functions were used to separately optimize magnitude and phase outputs

significantly improving speech quality while maintaining a lightweight parameter count. Additionally

a dynamic weight adjustment strategy was introduced that adaptively balanced the importance of multi-task loss functions by leveraging smoothed loss history and PESQ-aware feedback

effectively alleviating convergence bottlenecks caused by fixed-weight schemes. Experimental results on the VoiceBank+DEMAND and TIMIT datasets demonstrate that the proposed DW-MambaUNet outperforms various mainstream speech enhancement models in terms of PESQ

STOI

and MOS metrics

particularly under low signal-to-noise ratio conditions

showing strong enhancement performance and generalization ability. Ablation studies further confirm the effectiveness of the TF-Mamba module and DWConv structure in improving model performance. This study provides a novel perspective for the design of low-complexity and high-performance speech enhancement models

with both theoretical significance and practical value.

关键词

Keywords

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