ArticleScientific reports2024
Mixed T-domain and TF-domain Magnitude and Phase representations for GAN-based speech enhancement.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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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
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