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. [] Key Method In contrast to current techniques, we operate at the waveform level, training the model end-to-end, and incorporate 28 speakers and 40 different noise conditions into the same model, such that model parameters are shared across them. We evaluate the proposed model using an independent, unseen test set with two speakers and 20 alternative noise conditions. The enhanced samples confirm…

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