  In this work, we present CleanUNet, a causal speech denoising model on the
raw waveform. The proposed model is based on an encoder-decoder architecture
combined with several self-attention blocks to refine its bottleneck
representations, which is crucial to obtain good results. The model is
optimized through a set of losses defined over both waveform and
multi-resolution spectrograms. The proposed method outperforms the
state-of-the-art models in terms of denoised speech quality from various
objective and subjective evaluation metrics. We release our code and models at
https://github.com/nvidia/cleanunet.

Sound;Machine Learning;Audio and Speech Processing