3 citations · 4 across the 2 of their papers we have counts for
2 papers
cs.SD2024★ 3 cited
TDFNet: An Efficient Audio-Visual Speech Separation Model with Top-down Fusion
Samuel Pegg, Kai Li, Xiaolin Hu
Audio-visual speech separation has gained significant traction in recent years due to its potential applications in various fields such as speech recognition, diarization, scene an…
cs.SD2023★ 1 cited
RTFS-Net: Recurrent Time-Frequency Modelling for Efficient Audio-Visual Speech Separation
Samuel Pegg, Kai Li, Xiaolin Hu
Audio-visual speech separation methods aim to integrate different modalities to generate high-quality separated speech, thereby enhancing the performance of downstream tasks such a…