A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2019; you can also visit <a rel="external noopener" href="https://arxiv.org/pdf/1804.04121v2.pdf">the original URL</a>. The file type is <code>application/pdf</code>.
The Conversation: Deep Audio-Visual Speech Enhancement
[article]
<span title="2018-06-19">2018</span>
<i >
arXiv
</i>
<span class="release-stage" >pre-print</span>
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<span title="2018-04-11">2018</span>
<span class="release-stage" >pre-print</span>
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1804.04121v1">arXiv:1804.04121v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/xi422lo2xzbw7fghswvxfwonn4">fatcat:xi422lo2xzbw7fghswvxfwonn4</a> </span>
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1804.04121v1">arXiv:1804.04121v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/xi422lo2xzbw7fghswvxfwonn4">fatcat:xi422lo2xzbw7fghswvxfwonn4</a> </span>
Our goal is to isolate individual speakers from multi-talker simultaneous speech in videos. Existing works in this area have focussed on trying to separate utterances from known speakers in controlled environments. In this paper, we propose a deep audio-visual speech enhancement network that is able to separate a speaker's voice given lip regions in the corresponding video, by predicting both the magnitude and the phase of the target signal. The method is applicable to speakers unheard and
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<a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1804.04121v2">arXiv:1804.04121v2</a>
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... n during training, and for unconstrained environments. We demonstrate strong quantitative and qualitative results, isolating extremely challenging real-world examples.
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