{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/segan-speech-enhancement-generative","title":"SEGAN: Speech Enhancement Generative Adversarial Network","arxiv_id":"1703.09452","date":"2017-03-28","proceeding":null,"authors":["Santiago Pascual","Antonio Bonafonte","Joan Serrà"],"abstract":"Current speech enhancement techniques operate on the spectral domain and/or\nexploit some higher-level feature. The majority of them tackle a limited number\nof noise conditions and rely on first-order statistics. To circumvent these\nissues, deep networks are being increasingly used, thanks to their ability to\nlearn complex functions from large example sets. In this work, we propose the\nuse of generative adversarial networks for speech enhancement. In contrast to\ncurrent techniques, we operate at the waveform level, training the model\nend-to-end, and incorporate 28 speakers and 40 different noise conditions into\nthe same model, such that model parameters are shared across them. We evaluate\nthe proposed model using an independent, unseen test set with two speakers and\n20 alternative noise conditions. The enhanced samples confirm the viability of\nthe proposed model, and both objective and subjective evaluations confirm the\neffectiveness of it. With that, we open the exploration of generative\narchitectures for speech enhancement, which may progressively incorporate\nfurther speech-centric design choices to improve their performance.","url_abs":"http://arxiv.org/abs/1703.09452v3","url_pdf":"http://arxiv.org/pdf/1703.09452v3.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 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