  In this work, we present CleanUNet 2, a speech denoising model that combines
the advantages of waveform denoiser and spectrogram denoiser and achieves the
best of both worlds. CleanUNet 2 uses a two-stage framework inspired by popular
speech synthesis methods that consist of a waveform model and a spectrogram
model. Specifically, CleanUNet 2 builds upon CleanUNet, the state-of-the-art
waveform denoiser, and further boosts its performance by taking predicted
spectrograms from a spectrogram denoiser as the input. We demonstrate that
CleanUNet 2 outperforms previous methods in terms of various objective and
subjective evaluations.

Machine Learning;Sound;Audio and Speech Processing