  The most recent deep neural network (DNN) models exhibit impressive denoising
performance in the time-frequency (T-F) magnitude domain. However, the phase is
also a critical component of the speech signal that is easily overlooked. In
this paper, we propose a multi-branch dilated convolutional network (DCN) to
simultaneously enhance the magnitude and phase of noisy speech. A causal and
robust monaural speech enhancement system is achieved based on the
multi-objective learning framework of the complex spectrum and the ideal ratio
mask (IRM) targets. In the process of joint learning, the intermediate
estimation of IRM targets is used as a way of generating feature attention
factors to realize the information interaction between the two targets.
Moreover, the proposed multi-scale dilated convolution enables the DCN model to
have a more efficient temporal modeling capability. Experimental results show
that compared with other state-of-the-art models, this model achieves better
speech quality and intelligibility with less computation.
