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Previous research in speech enhancement has mostly focused on modeling timeor time-frequency domain information alone, with little consideration given tothe potential benefits of simultaneously modeling both domains. Since thesedomains contain complementary information, combining them may improve theperformance of the model. In this letter, we propose a new approach tosimultaneously model time and time-frequency domain information in a singlemodel. We begin with the DPT-FSNet (causal version) model as a baseline andmodify the encoder structure by replacing the original encoder with threeseparate encoders, each dedicated to modeling time-domain, real-imaginary, andmagnitude information, respectively. Additionally, we introduce a featurefusion module both before and after the dual-path processing blocks to betterleverage information from the different domains. The outcomes of ourexperiments reveal that the proposed approach achieves superior performancecompared to existing state-of-the-art causal models, while preserving arelatively compact model size and low computational complexity.

ForkNet: Simultaneous Time and Time-Frequency Domain Modeling for Speech Enhancement