AV2WAV: Diffusion-Based Re-Synthesis from Continuous Self-Supervised Features for Audio-Visual Speech Enhancement-Reference-Cited by-同舟云学术

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AV2WAV: Diffusion-Based Re-Synthesis from Continuous Self-Supervised Features for Audio-Visual Speech Enhancement

Published:2024-04-14 Issue: Volume: Page:10806-10810
ISSN:
Container-title:ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
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Author:

Chou Ju-Chieh¹,Chien Chung-Ming¹,Livescu Karen¹

Affiliation:

1. Toyota Technological Institute,Chicago

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10445798/10445803/10446625.pdf?arnumber=10446625

Reference28 articles.

1. Audio-Visual Speech Enhancement Using Multimodal Deep Convolutional Neural Networks

2. Audio-Visual Speech Enhancement and Separation by Utilizing Multi-Modal Self-Supervised Embeddings

3. The Conversation: Deep Audio-Visual Speech Enhancement

4. VisualVoice: Audio-Visual Speech Separation with Cross-Modal Consistency

5. Audio-Visual Speech Codecs: Rethinking Audio-Visual Speech Enhancement by Re-Synthesis

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