  Subband-based approaches process subbands in parallel through the model with
shared parameters to learn the commonality of local spectrums for noise
reduction. In this way, they have achieved remarkable results with fewer
parameters. However, in some complex environments, the lack of global spectral
information has a negative impact on the performance of these subband-based
approaches. To this end, this paper introduces the subband interaction as a new
way to complement the subband model with the global spectral information such
as cross-band dependencies and global spectral patterns, and proposes a new
lightweight single-channel speech enhancement framework called Interactive
Subband Network (Inter-SubNet). Experimental results on DNS Challenge -
Interspeech 2021 dataset show that the proposed Inter-SubNet yields a
significant improvement over the subband model and outperforms other
state-of-the-art speech enhancement approaches, which demonstrate the
effectiveness of subband interaction.
