Efficient adversarial audio synthesis via progressive upsampling

Abstract

This paper proposes a novel generative model called PUGAN, which progressively synthesizes high-quality audio in a raw waveform. Progressive upsampling GAN (PUGAN) leverages the progressive generation of higher-resolution output by stacking multiple encoder-decoder architectures. Compared to an existing state-of-the-art model called WaveGAN, which uses a single decoder architecture, our model generates audio signals and converts them to a higher resolution in a progressive manner, while using a significantly smaller number of parameters, e.g., 3.17x smaller for 16 kHz output, than WaveGAN. Our experiments show that the audio signals can be generated in real time with a comparable quality to that of WaveGAN in terms of the inception scores and human perception.