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SEMamba++: A General Speech Restoration Framework Leveraging Global, Local, and Periodic Spectral Patterns

arXiv:2603.11669v2 Announce Type: replace-cross 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.

arXiv CS 1d ago