用Mamba替代注意力机制，实现高效高质语音增强
Mamba-SEUNet: Mamba UNet for Monaural Speech Enhancement
- 用双向Mamba建模语音多尺度时序依赖
- 在VCTK+DEMAND数据集上达PESQ 3.59，融合后升至3.73
- 适合追求低计算量与高音质的实时语音增强场景
近期语音增强研究中，变压器及其变体已成为主流方法。然而，自注意力机制的二次复杂度对实际部署带来一定限制。Mamba作为一种新型状态空间模型（SSM），凭借其对长序列的建模能力与较低的计算复杂度，在自然语言处理和计算机视觉领域得到广泛应用。本文提出Mamba-SEUNet，一种将Mamba与U-Net结合用于语音增强的新架构。通过在不同分辨率下使用双向Mamba建模语音信号的前向与后向依赖，并引入跳跃连接以捕捉多尺度信息，该方法实现了当前最优性能。在VCTK+DEMAND数据集上的实验结果表明，Mamba-SEUNet取得3.59的PESQ得分，同时保持低计算复杂度。当与感知对比拉伸技术结合后，PESQ进一步提升至3.73。
原文摘要 · Abstract (English)
In recent speech enhancement (SE) research, transformer and its variants have emerged as the predominant methodologies. However, the quadratic complexity of the self-attention mechanism imposes certain limitations on practical deployment. Mamba, as a novel state-space model (SSM), has gained widespread application in natural language processing and computer vision due to its strong capabilities in modeling long sequences and relatively low computational complexity. In this work, we introduce Mamba-SEUNet, an innovative architecture that integrates Mamba with U-Net for SE tasks. By leveraging bidirectional Mamba to model forward and backward dependencies of speech signals at different resolutions, and incorporating skip connections to capture multi-scale information, our approach achieves state-of-the-art (SOTA) performance. Experimental results on the VCTK+DEMAND dataset indicate that Mamba-SEUNet attains a PESQ score of 3.59, while maintaining low computational complexity. When combined with the Perceptual Contrast Stretching technique, Mamba-SEUNet further improves the PESQ score to 3.73.
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