A Window-based Discriminator is a type of discriminator for generative adversarial networks. It is analogous to a PatchGAN but designed for audio. While a standard GAN discriminator learns to classify between distributions of entire audio sequences, window-based discriminator learns to classify between distribution of small audio chunks. Since the discriminator loss is computed over the overlapping windows where each window is very large (equal to the receptive field of the discriminator), the model learns to maintain coherence across patches.