8 citations · 19 across the 19 of their papers we have counts for
5 papers · 1 filter
Injecting Spatial Information for Monaural Speech Enhancement via Knowledge Distillation
Xinmeng Xu, Weiping Tu, Yuhong Yang
Monaural speech enhancement (SE) provides a versatile and cost-effective approach to SE tasks by utilizing recordings from a single microphone. However, the monaural SE lags perfor…
Improving Visual Speech Enhancement Network by Learning Audio-visual Affinity with Multi-head Attention
Xinmeng Xu, Yang Wang, Jie Jia +2
Audio-visual speech enhancement system is regarded as one of promising solutions for isolating and enhancing speech of desired speaker. Typical methods focus on predicting clean sp…
GLD-Net: Improving Monaural Speech Enhancement by Learning Global and Local Dependency Features with GLD Block
Xinmeng Xu, Yang Wang, Jie Jia +2
For monaural speech enhancement, contextual information is important for accurate speech estimation. However, commonly used convolution neural networks (CNNs) are weak in capturing…
Improving Dual-Microphone Speech Enhancement by Learning Cross-Channel Features with Multi-Head Attention
Xinmeng Xu, Rongzhi Gu, Yuexian Zou
Hand-crafted spatial features, such as inter-channel intensity difference (IID) and inter-channel phase difference (IPD), play a fundamental role in recent deep learning based dual…
U-Former: Improving Monaural Speech Enhancement with Multi-head Self and Cross Attention
Xinmeng Xu, Jianjun Hao
For supervised speech enhancement, contextual information is important for accurate spectral mapping. However, commonly used deep neural networks (DNNs) are limited in capturing te…