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\begin{document}
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\title{CMGAN: Conformer-Based Metric-GAN for Monaural Speech Enhancement}



\author[1]{Sherif Abdulatif }%
\author[1]{Ruizhe Cao }%
\author[1]{Bin Yang }%
\affil[1]{Affiliation not available}%


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  \date{May 07, 2026}


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\begin{abstract}
In this work, we further develop the conformer-based metric generative
adversarial network (CMGAN) model for speech enhancement (SE) in the
time-frequency (TF) domain. This paper builds on our previous work but
takes a more in-depth look by conducting extensive ablation studies on
model inputs and architectural design choices. We rigorously tested the
generalization ability of the model to unseen noise types and
distortions. We have fortified our claims through DNS-MOS measurements
and listening tests. Rather than focusing exclusively on the speech
denoising task, we extend this work to address the dereverberation and
super-resolution tasks. This necessitated exploring various
architectural changes, specifically metric discriminator scores and
masking techniques. It is essential to highlight that this is among the
earliest works that attempted complex TF-domain super-resolution. Our
findings show that CMGAN outperforms existing state-of-the-art methods
in the three major speech enhancement tasks:~ denoising,
dereverberation, and super-resolution. For example, in the denoising
task using the Voice Bank+DEMAND dataset, CMGAN notably exceeded the
performance of prior models, attaining a PESQ score of 3.41 and an SSNR
of 11.10 dB. Audio samples and CMGAN implementations are available
online.%
\end{abstract}%



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