GANSynth: Adversarial Neural Audio Synthesis

ICLR 2019 Jesse EngelKumar Krishna AgrawalShuo ChenIshaan GulrajaniChris DonahueAdam Roberts

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... (read more)

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