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.

翻译：基于子带的方法通过共享参数的模型并行处理子带，以学习局部频谱的共性实现降噪。这类方法以较少的参数取得了显著效果。然而，在某些复杂环境下，全局频谱信息的缺失会对这些基于子带的方法的性能产生负面影响。为此，本文引入子带交互这一新机制，通过跨频带依赖关系和全局频谱模式等全局频谱信息来补充子带模型，并提出了一种名为交互式子带网络（Inter-SubNet）的新型轻量级单通道语音增强框架。在DNS Challenge - Interspeech 2021数据集上的实验结果表明，所提出的Inter-SubNet相较于子带模型取得了显著提升，并优于其他当前最优的语音增强方法，验证了子带交互的有效性。