SEGAN: Speech Enhancement Generative Adversarial Network

@inproceedings{Pascual2017SEGANSE,
  title={SEGAN: Speech Enhancement Generative Adversarial Network},
  author={Santiago Pascual and Antonio Bonafonte and Joan Serr{\`a}},
  booktitle={INTERSPEECH},
  year={2017}
}
Current speech enhancement techniques operate on the spectral domain and/or exploit some higher-level feature. The majority of them tackle a limited number of noise conditions and rely on first-order statistics. To circumvent these issues, deep networks are being increasingly used, thanks to their ability to learn complex functions from large example sets. In this work, we propose the use of generative adversarial networks for speech enhancement. In contrast to current techniques, we operate at… CONTINUE READING
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