4 citations · 5 across the 7 of their papers we have counts for
7 papers
Improving Speech Enhancement by Integrating Inter-Channel and Band Features with Dual-branch Conformer
Jizhen Li, Xinmeng Xu, Weiping Tu +2
Recent speech enhancement methods based on convolutional neural networks (CNNs) and transformer have been demonstrated to efficaciously capture time-frequency (T-F) information on…
PCNN: A Lightweight Parallel Conformer Neural Network for Efficient Monaural Speech Enhancement
Xinmeng Xu, Weiping Tu, Yuhong Yang
Convolutional neural networks (CNN) and Transformer have wildly succeeded in multimedia applications. However, more effort needs to be made to harmonize these two architectures eff…
CQNV: A combination of coarsely quantized bitstream and neural vocoder for low rate speech coding
Youqiang Zheng, Li Xiao, Weiping Tu +2
Recently, speech codecs based on neural networks have proven to perform better than traditional methods. However, redundancy in traditional parameter quantization is visible within…
All Information is Necessary: Integrating Speech Positive and Negative Information by Contrastive Learning for Speech Enhancement
Xinmeng Xu, Weiping Tu, Chang Han +1
Monaural speech enhancement (SE) is an ill-posed problem due to the irreversible degradation process. Recent methods to achieve SE tasks rely solely on positive information, e.g.,…
Selector-Enhancer: Learning Dynamic Selection of Local and Non-local Attention Operation for Speech Enhancement
Xinmeng Xu, Weiping Tu, Yuhong Yang
Attention mechanisms, such as local and non-local attention, play a fundamental role in recent deep learning based speech enhancement (SE) systems. However, natural speech contains…
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…