Mixed T-domain and TF-domain Magnitude and Phase representations for GAN-based speech enhancement

Abstract

Deep learning has made significant advancements in speech enhancement, which plays a crucial role in improving the quality of speech signals in noisy conditions. In this paper, we propose a new approach called M-DGAN, which introduces a time (T)-domain encoder-decoder structure with rich channel representations into the time-frequency (TF)-domain generator framework, resulting in a new generator structure with mixed magnitude and phase representations in the T and TF-domains. The proposed mixed T-domain and TF-domain generator, incorporating the cascaded reworked conformer (CRC) structure, exhibits improved modeling capability and adaptability. Test results on the Voice Bank + DEMAND public dataset show that our method achieves the highest score with PSEQ=3.52\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$PSEQ=3.52$$\\end{document} and performs well on all the remaining metrics when compared to the current state-of-the-art methods. In addition, tests on the NISQA_TEST_LIVETALK real dataset of the NISQA Corpus show the breadth and robustness of our model on speech enhancement tasks.