Abstract
General speech restoration demands techniques that can interpret complex speech structures under various distortions. While State-Space Models like SEMamba have advanced the state-of-the-art in speech denoising, they are not inherently optimized for critical speech characteristics, such as spectral periodicity or multi-resolution frequency analysis. In this work, we introduce an architecture tailored to incorporate speech-specific features as inductive biases. In particular, we propose Frequency GLP, a frequency feature extraction block that effectively and efficiently leverages the properties of frequency bins. Then, we design a multi-resolution parallel time-frequency dual-processing block to capture diverse spectral patterns, and a learnable mapping to further enhance model performance. With all our ideas combined, the proposed SEMamba++ achieves the best performance among multiple baseline models while remaining computationally efficient.
Abstract (translated)
一般性的语音恢复技术需要能够在各种失真条件下解析复杂的语音结构的技术。虽然状态空间模型（如SEMamba）已经在语音降噪方面取得了最先进的成果，但它们并不固有地针对诸如频谱周期性或多分辨率频率分析等关键语音特征进行优化。在这项工作中，我们介绍了一种专门设计来整合特定于语音的特性作为归纳偏差的架构。具体来说，我们提出了频率GLP（Frequency GLP），这是一种频率特征提取模块，能够有效且高效地利用频率带的属性。然后，我们设计了一个多分辨率并行时频双处理块，以捕捉多样化的频谱模式，并引入了一种可学习映射来进一步提升模型性能。通过结合所有这些想法，所提出的SEMamba++在保持计算效率的同时，在多个基线模型中取得了最佳性能。
URL
https://arxiv.org/abs/2603.11669
