8 citations · 19 across the 19 of their papers we have counts for
6 papers · 1 filter
SE Territory: Monaural Speech Enhancement Meets the Fixed Virtual Perceptual Space Mapping
Xinmeng Xu, Yuhong Yang, Weiping Tu
Monaural speech enhancement has achieved remarkable progress recently. However, its performance has been constrained by the limited spatial cues available at a single microphone. T…
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…
Exploring the Interactions between Target Positive and Negative Information for Acoustic Echo Cancellation
Chang Han, Xinmeng Xu, Weiping Tu +2
Acoustic echo cancellation (AEC) aims to remove interference signals while leaving near-end speech least distorted. As the indistinguishable patterns between near-end speech and in…
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…