This work aims to study a scalable state-space model (SSM), Mamba, for the
speech enhancement (SE) task. We exploit a Mamba-based regression model to
characterize speech signals and build an SE system upon Mamba, termed SEMamba.
We explore the properties of Mamba by integrating it as the core model in both
basic and advanced SE systems, along with utilizing signal-level distances as
well as metric-oriented loss functions. SEMamba demonstrates promising results
and attains a PESQ score of 3.55 on the VoiceBank-DEMAND dataset. When combined
with the perceptual contrast stretching technique, the proposed SEMamba yields
a new state-of-the-art PESQ score of 3.69.

研究了一种可扩展的状态空间模型（SSM）Mamba，用于语音增强（SE）任务，利用基于 Mamba 的回归模型来表征语音信号，并在 Mamba 上构建了一个 SE 系统，命名为 SEMamba，在基本和高级 SE 系统中集成了 Mamba 的属性，并利用信号级距离和基于度量的损失函数。SEMamba 表现出有希望的结果，并在 VoiceBank-DEMAND 数据集上获得了 3.55 的 PESQ 分数。当与感知对比拉伸技术结合使用时，所提出的 SEMamba 获得了 3.69 的最新的 PESQ 分数。