Abstract

Isolating the voice of a specific person while filtering out other voices or background noises is challenging when video is shot in noisy environments. We propose audio-visual methods to isolate the voice of a single speaker and eliminate unrelated sounds. First, face motions captured in the video are used to estimate the speaker's voice, by passing the silent video frames through a video-to-speech neural network-based model. Then the speech predictions are applied as a filter on the noisy input audio. This approach avoids using mixtures of sounds in the learning process, as the number of such possible mixtures is huge, and would inevitably bias the trained model. We evaluate our method on two audio-visual datasets, GRID and TCD-TIMIT, and show that our method attains significant SDR and PESQ improvements over the raw video-to-speech predictions, and a well-known audio-only method.

IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2018

Supplementary video

The following video contains a few examples of separated and enhanced speech using our models.


BibTeX

@inproceedings{gabbay2018seeing,
  author    = {Aviv Gabbay and
               Ariel Ephrat and
               Tavi Halperin and
               Shmuel Peleg},
  title     = {Seeing Through Noise: Visually Driven Speaker Separation And Enhancement},
  booktitle = {{ICASSP}},
  pages     = {3051--3055},
  publisher = {{IEEE}},
  year      = {2018}
}
	

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