MelGAN
MelGAN
**MelGAN** is a non-autoregressive feed-forward convolutional architecture to perform audio waveform generation in a [GAN](https://paperswithcode.com/method/gan) setup. The architecture is a fully convolutional feed-forward network with mel-spectrogram $s$ as input and raw waveform $x$ as output. Since the mel-spectrogram is at
a 256× lower temporal resolution, the authors use a stack of transposed convolutional layers to upsample the input sequence. Each transposed convolutional layer is followed by a stack of residual blocks with dilated convolutions. Unlike traditional GANs, the MelGAN generator does not use a global noise vector as input.

To deal with 'checkerboard artifacts' in audio, instead of using [PhaseShuffle](https://paperswithcode.com/method/phase-shuffle), MelGAN uses kernel-size as a multiple of stride.

[Weight normalization](https://paperswithcode.com/method/weight-normalization) is used for normalization. A [window-based discriminator](https://paperswithcode.com/method/window-based-discriminator), similar to a [PatchGAN](https://paperswithcode.com/method/patchgan) is used for the discriminator.
MelGAN: Generative Adversarial Networks for Conditional Waveform Synthesis
https://paperswithcode.com/paper/melgan-generative-adversarial-networks-for
MelGAN: Generative Adversarial Networks for Conditional Waveform Synthesis