  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.
