  In speech enhancement (SE), phase estimation is important for perceptual
quality, so many methods take clean speech's complex short-time Fourier
transform (STFT) spectrum or the complex ideal ratio mask (cIRM) as the
learning target. To predict these complex targets, the common solution is to
design a complex neural network, or use a real network to separately predict
the real and imaginary parts of the target. But in this paper, we propose to
use a real network to estimate the magnitude mask and normalized cIRM, which
not only avoids the significant increase of the model complexity caused by
complex networks, but also shows better performance than previous phase
estimation methods. Meanwhile, we devise a parallel sequence modeling (PSM)
block to improve the RNN block in the convolutional recurrent network
(CRN)-based SE model. We name our method as magnitude-and-phase-aware and
PSM-based CRN (MPCRN). The experimental results illustrate that our MPCRN has
superior SE performance.
