Speech Denoising with Deep Feature Losses

@article{Germain2019SpeechDW,
  title={Speech Denoising with Deep Feature Losses},
  author={F. Germain and Qifeng Chen and V. Koltun},
  journal={ArXiv},
  year={2019},
  volume={abs/1806.10522}
}
We present an end-to-end deep learning approach to denoising speech signals by processing the raw waveform directly. [...] Key Result The advantage of the new approach is particularly pronounced for the hardest data with the most intrusive background noise, for which denoising is most needed and most challenging.Expand
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