Audio signals are sampled at high temporal resolutions, and learning to
synthesize audio requires capturing structure across a range of timescales.
Generative adversarial networks (GANs) have seen wide success at generating
images that are both locally and globally coherent, but they have seen little
application to audio generation. In this paper we introduce WaveGAN, a first
attempt at applying GANs to unsupervised synthesis of raw-waveform audio.
WaveGAN is capable of synthesizing one second slices of audio waveforms with
global coherence, suitable for sound effect generation. Our experiments
demonstrate that, without labels, WaveGAN learns to produce intelligible words
when trained on a small-vocabulary speech dataset, and can also synthesize
audio from other domains such as drums, bird vocalizations, and piano. We
compare WaveGAN to a method which applies GANs designed for image generation on
image-like audio feature representations, finding both approaches to be
promising.

本篇论文介绍了 WaveGAN，这是将 GANs 应用到无监督的原始音频波形合成中的第一个尝试。WaveGAN 能够合成具有全局一致性的音频波形，适用于音效生成，并且实验结果表明，它能够在没有标签的情况下学习生成一些可理解的单词和其他领域（如鸟类鸣叫、钢琴和鼓声等）的音频。