  Recently, multi-stage systems have stood out among deep learning-based speech
enhancement methods. However, these systems are always high in complexity,
requiring millions of parameters and powerful computational resources, which
limits their application for real-time processing in low-power devices.
Besides, the contribution of various influencing factors to the success of
multi-stage systems remains unclear, which presents challenges to reduce the
size of these systems. In this paper, we extensively investigate a lightweight
two-stage network with only 560k total parameters. It consists of a Mel-scale
magnitude masking model in the first stage and a complex spectrum mapping model
in the second stage. We first provide a consolidated view of the roles of gain
power factor, post-filter, and training labels for the Mel-scale masking model.
Then, we explore several training schemes for the two-stage network and provide
some insights into the superiority of the two-stage network. We show that the
proposed two-stage network trained by an optimal scheme achieves a performance
similar to a four times larger open source model DeepFilterNet2.
