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 the Global, Local, and Periodic (GLP) module, 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.
From: Yong Joon Lee [view email]
[v1]
Thu, 12 Mar 2026 08:37:28 UTC (725 KB)
[v2]
Mon, 8 Jun 2026 07:16:00 UTC (725 KB)
[v3]
Wed, 26 Aug 2026 01:46:29 UTC (726 KB)
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