  With recent advances of diffusion model, generative speech enhancement (SE)
has attracted a surge of research interest due to its great potential for
unseen testing noises. However, existing efforts mainly focus on inherent
properties of clean speech, underexploiting the varying noise information in
real world. In this paper, we propose a noise-aware speech enhancement (NASE)
approach that extracts noise-specific information to guide the reverse process
in diffusion model. Specifically, we design a noise classification (NC) model
to produce acoustic embedding as a noise conditioner to guide the reverse
denoising process. Meanwhile, a multi-task learning scheme is devised to
jointly optimize SE and NC tasks to enhance the noise specificity of
conditioner. NASE is shown to be a plug-and-play module that can be generalized
to any diffusion SE models. Experiments on VB-DEMAND dataset show that NASE
effectively improves multiple mainstream diffusion SE models, especially on
unseen noises.
