  Recent advancement in Generative Adversarial Networks in speech synthesis
domain[3],[2] have shown, that it's possible to train GANs [8] in a reliable
manner for high quality coherent waveform generation from mel-spectograms. We
propose that it is possible to transfer the MelGAN's [3] robustness in learning
speech features to speech enhancement and noise reduction domain without any
model modification tasks. Our proposed method generalizes over multi-speaker
speech dataset and is able to robustly handle unseen background noises during
the inference. Also, we show that by increasing the batch size for this
particular approach not only yields better speech results, but generalizes over
multi-speaker dataset easily and leads to faster convergence. Additionally, it
outperforms previous state of the art GAN approach for speech enhancement SEGAN
[5] in two domains: 1. quality ; 2. speed. Proposed method runs at more than
100x faster than realtime on GPU and more than 2x faster than real time on CPU
without any hardware optimization tasks, right at the speed of MelGAN [3].
