Generative Audio Models

# MelGAN

Introduced by Kumar et al. in MelGAN: Generative Adversarial Networks for Conditional Waveform Synthesis

MelGAN is a non-autoregressive feed-forward convolutional architecture to perform audio waveform generation in a 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, MelGAN uses kernel-size as a multiple of stride.

Weight normalization is used for normalization. A window-based discriminator, similar to a PatchGAN is used for the discriminator.

#### Papers

Paper Code Results Date Stars