**SpecGAN** is a generative adversarial network method for spectrogram-based, frequency-domain audio generation. The problem is suited for GANs designed for image generation. The model can be approximately inverted. 

To process audio into suitable spectrograms, the authors perform the short-time Fourier transform with 16 ms windows and 8ms stride, resulting in 128 frequency bins, linearly spaced from 0 to 8 kHz. They take the magnitude of the resultant spectra and scale amplitude values logarithmically to better-align with human perception. They then normalize each frequency bin to have zero mean and unit variance. They clip the spectra to $3$ standard deviations and rescale to $\left[−1, 1\right]$.

They then use the [DCGAN](https://paperswithcode.com/method/dcgan) approach on the result spectra.

**ProphetNet** is a sequence-to-sequence pre-training model that introduces a novel self-supervised objective named future n-gram prediction and the proposed n-stream self-attention mechanism. Instead of optimizing one-step-ahead prediction in the traditional sequence-to-sequence model, the ProphetNet is optimized by $n$-step ahead prediction that predicts the next $n$ tokens simultaneously based on previous context tokens at each time step. The future n-gram prediction explicitly encourages the model to plan for the future tokens and further help predict multiple future tokens.

ProphetNet

ProphetNet: Predicting Future N-gram for Sequence-to-Sequence Pre-training

SpecGAN

Adversarial Audio Synthesis

**Differential Diffusion** is an enhancement of image-to-image diffusion models that adds the ability to control the amount of change applied to each image fragment via a change map.

Source	Adversarial Audio Synthesis
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com