Efficient audio synthesis is an inherently difficult machine learning task, as human perception is sensitive to both global structure and fine-scale waveform coherence. Autoregressive models, such as WaveNet, model local structure at the expense of global latent structure and slow iterative sampling, while Generative Adversarial Networks (GANs), have global latent conditioning and efficient parallel sampling, but struggle to generate locally-coherent audio waveforms. Herein, we demonstrate that GANs can in fact generate high-fidelity and locally-coherent audio by modeling log magnitudes and instantaneous frequencies  with sufficient frequency resolution in the spectral domain. Through extensive empirical investigations on the NSynth dataset, we demonstrate that GANs are able to outperform strong WaveNet baselines on automated and human evaluation metrics, and efficiently generate audio several orders of magnitude faster than their autoregressive counterparts.

High-quality audio synthesis with GANs Proposes an approach that uses GAN framework to generate audio through modeling log magnitudes and instantaneous frequencies with sufficient frequency resolution in the spectral domain.  A strategy to generate audio samples from noise with GANs, with changes to the architecture and representation necessary to generate convincing audio that contains an interpretable latent code. Presents a simple idea for better representing audio data so that convolutional models such as generative adversarial networks can be applied