  Recent single-channel speech enhancement methods based on deep neural
networks (DNNs) have achieved remarkable results, but there are still
generalization problems in real scenes. Like other data-driven methods,
DNN-based speech enhancement models produce significant performance degradation
on untrained data. In this study, we make full use of the contribution of
multi-target joint learning to the model generalization capability, and propose
a lightweight and low-computing dilated convolutional network (DCN) model for a
more robust speech denoising task. Our goal is to integrate the masking target,
the mapping target, and the parameters of the traditional speech enhancement
estimator into a DCN model to maximize their complementary advantages. To do
this, we build a multi-stage learning framework to deal with multiple targets
in stages to achieve their joint learning, namely `MT-in-MS'. Our experimental
results show that compared with the state-of-the-art time domain and
time-frequency domain models, this proposed low-cost DCN model can achieve
better generalization performance in speaker, noise, and channel mismatch
cases.
