  Previous research in speech enhancement has mostly focused on modeling time
or time-frequency domain information alone, with little consideration given to
the potential benefits of simultaneously modeling both domains. Since these
domains contain complementary information, combining them may improve the
performance of the model. In this letter, we propose a new approach to
simultaneously model time and time-frequency domain information in a single
model. We begin with the DPT-FSNet (causal version) model as a baseline and
modify the encoder structure by replacing the original encoder with three
separate encoders, each dedicated to modeling time-domain, real-imaginary, and
magnitude information, respectively. Additionally, we introduce a feature
fusion module both before and after the dual-path processing blocks to better
leverage information from the different domains. The outcomes of our
experiments reveal that the proposed approach achieves superior performance
compared to existing state-of-the-art causal models, while preserving a
relatively compact model size and low computational complexity.
