6 citations · 11 across the 5 of their papers we have counts for
5 papers
Selective State Space Model for Monaural Speech Enhancement
Moran Chen, Qiquan Zhang, Mingjiang Wang +4
Voice user interfaces (VUIs) have facilitated the efficient interactions between humans and machines through spoken commands. Since real-word acoustic scenes are complex, speech en…
FB-MSTCN: A Full-Band Single-Channel Speech Enhancement Method Based on Multi-Scale Temporal Convolutional Network
Zehua Zhang, Lu Zhang, Xuyi Zhuang +3
In recent years, deep learning-based approaches have significantly improved the performance of single-channel speech enhancement. However, due to the limitation of training data an…
Incorporating Multi-Target in Multi-Stage Speech Enhancement Model for Better Generalization
Lu Zhang, Mingjiang Wang, Andong Li +2
Recent single-channel speech enhancement methods based on deep neural networks (DNNs) have achieved remarkable results, but there are still generalization problems in real scenes.…
Deep Interaction between Masking and Mapping Targets for Single-Channel Speech Enhancement
Lu Zhang, Mingjiang Wang, Zehua Zhang +1
The most recent deep neural network (DNN) models exhibit impressive denoising performance in the time-frequency (T-F) magnitude domain. However, the phase is also a critical compon…
Monaural Speech Enhancement Using a Multi-Branch Temporal Convolutional Network
Qiquan Zhang, Aaron Nicolson, Mingjiang Wang +2
Deep learning has achieved substantial improvement on single-channel speech enhancement tasks. However, the performance of multi-layer perceptions (MLPs)-based methods is limited b…