ISCA Archive Interspeech 2021
ISCA Archive Interspeech 2021

NU-Wave: A Diffusion Probabilistic Model for Neural Audio Upsampling

Junhyeok Lee, Seungu Han

In this work, we introduce NU-Wave, the first neural audio upsampling model to produce waveforms of sampling rate 48kHz from coarse 16kHz or 24kHz inputs, while prior works could generate only up to 16kHz. NU-Wave is the first diffusion probabilistic model for audio super-resolution which is engineered based on neural vocoders. NU-Wave generates high-quality audio that achieves high performance in terms of signal-to-noise ratio (SNR), log-spectral distance (LSD), and accuracy of the ABX test. In all cases, NU-Wave outperforms the baseline models despite the substantially smaller model capacity (3.0M parameters) than baselines (5.4–21%). The audio samples of our model are publicly available, and the code will be made available soon.


doi: 10.21437/Interspeech.2021-36

Cite as: Lee, J., Han, S. (2021) NU-Wave: A Diffusion Probabilistic Model for Neural Audio Upsampling. Proc. Interspeech 2021, 1634-1638, doi: 10.21437/Interspeech.2021-36

@inproceedings{lee21c_interspeech,
  author={Junhyeok Lee and Seungu Han},
  title={{NU-Wave: A Diffusion Probabilistic Model for Neural Audio Upsampling}},
  year=2021,
  booktitle={Proc. Interspeech 2021},
  pages={1634--1638},
  doi={10.21437/Interspeech.2021-36}
}