Speech Denoising in the Waveform Domain with Self-Attention
Abstract
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.
Cite
@article{arxiv.2202.07790,
title = {Speech Denoising in the Waveform Domain with Self-Attention},
author = {Zhifeng Kong and Wei Ping and Ambrish Dantrey and Bryan Catanzaro},
journal= {arXiv preprint arXiv:2202.07790},
year = {2022}
}
Comments
Published in ICASSP 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). Listen to audio samples from CleanUNet at: https://cleanunet.github.io/