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

Junhyeok Lee, Seungu Han
4/6/2021
eess.AScs.AIcs.LG

Official code: maum-ai/nuwave (283 stars, BSD-3-Clause, PyTorch, last commit 2022-07-22).

Abstract

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 available at https://mindslab-ai.github.io/nuwave, and the code will be made available soon.

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Code Implementations(1)

maum-ai/nuwaveOfficial100%
PyTorchBSD-3-Clause

NU-Wave: A Diffusion Probabilistic Model for Neural Audio Upsampling @ INTERSPEECH 2021

28320Python, DockerfileMar 30, 20214 years agoBSD-3-Clause
deep-generative-modeldeep-learningneural-audio-upsamplingpytorchsuper-resolution+1 more

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Cite this paper

@article{lee2021nuwave,
  title  = {NU-Wave: A Diffusion Probabilistic Model for Neural Audio Upsampling},
  author = {Junhyeok Lee and Seungu Han},
  year   = {2021},
  eprint = {2104.02321},
  archivePrefix = {arXiv},
  url    = {https://arxiv.org/abs/2104.02321}
}

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