NU-GAN: High resolution neural upsampling with GAN

By Rithesh Kumar, Kundan Kumar, Vicki Anand, Yoshua Bengio, Aaron C. Courville. 2020

35 citations recorded by the source corpus.

In this paper, we propose NU-GAN, a new method for resampling audio from lower to higher sampling rates (upsampling). Audio upsampling is an important problem since productionizing generative speech technology requires operating at high sampling rates. Such applications use audio at a resolution of 44.1 kHz or 48 kHz, whereas current speech synthesis methods are equipped to handle a maximum of 24 kHz resolution. NU-GAN takes a leap towards solving audio upsampling as a separate component in the text-to-speech (TTS) pipeline by leveraging techniques for audio generation using GANs. ABX preference tests indicate that our NU-GAN resampler is capable of resampling 22 kHz to 44.1 kHz audio that is distinguishable from original audio only 7.4% higher than random chance for single speaker dataset, and 10.8% higher than chance for multi-speaker dataset.

Subfields: Artificial Intelligence, Signal Processing

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Citations in the corpus

TTS-Portuguese Corpus: a corpus for speech synthesis in Brazilian Portuguese (2020, methodology): “In addition, [41] showed that it is possible to obtain a 44 kHz TTS model by training the NU-GAN model on a dataset sampled at 44 kHz.”