  Attention mechanism has been widely utilized in speech enhancement (SE)
because theoretically it can effectively model the long-term inherent
connection of signal both in time domain and spectrum domain. However, the
generally used global attention mechanism might not be the best choice since
the adjacent information naturally imposes more influence than the far-apart
information in speech enhancement. In this paper, we validate this conjecture
by replacing attention with RNN in two typical state-of-the-art (SOTA) models,
multi-scale temporal frequency convolutional network (MTFAA) with axial
attention and conformer-based metric-GAN network (CMGAN).
