16 citations · 36 across the 14 of their papers we have counts for
6 papers · 1 filter
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…
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…
IIANet: An Intra- and Inter-Modality Attention Network for Audio-Visual Speech Separation
Kai Li, Runxuan Yang, Fuchun Sun +1
Recent research has made significant progress in designing fusion modules for audio-visual speech separation. However, they predominantly focus on multi-modal fusion at a single te…
MIXPGD: Hybrid Adversarial Training for Speech Recognition Systems
Aminul Huq, Weiyi Zhang, Xiaolin Hu
Automatic speech recognition (ASR) systems based on deep neural networks are weak against adversarial perturbations. We propose mixPGD adversarial training method to improve the ro…
An efficient encoder-decoder architecture with top-down attention for speech separation
Kai Li, Runxuan Yang, Xiaolin Hu
Deep neural networks have shown excellent prospects in speech separation tasks. However, obtaining good results while keeping a low model complexity remains challenging in real-wor…
On the Use of Deep Mask Estimation Module for Neural Source Separation Systems
Kai Li, Xiaolin Hu, Yi Luo
Most of the recent neural source separation systems rely on a masking-based pipeline where a set of multiplicative masks are estimated from and applied to a signal representation o…