4 citations · 4 across the 19 of their papers we have counts for
19 papers
ProLombard: Structured Multi-Scale Modeling for Normal-to-Lombard Speech Conversion
Hongyang Chen, Xinmeng Xu, Youqiang Zheng +5
Normal-to-Lombard (N2L) speech conversion aims to improve speech intelligibility in noisy environments by transforming normal speech into Lombard-style speech while preserving ling…
Noisy Environment Adaptation of Neural Speech Codec via Focal Mask and Noise Feature Separation
Shaokai Li, Weiping Tu, Yuhong Yang
Neural speech codec has attracted extensive attention for high-quality reconstruction at low-bitrate. However, real-world noise severely degrades its performance and hinders high-q…
GEM-TFL: Bridging Weak and Full Supervision for Forgery Localization through EM-Guided Decomposition and Temporal Refinement
Xiaodong Zhu, Yuanming Zheng, Suting Wang +4
Temporal Forgery Localization (TFL) aims to precisely identify manipulated segments within videos or audio streams, providing interpretable evidence for multimedia forensics and se…
DeformTrace: A Deformable State Space Model with Relay Tokens for Temporal Forgery Localization
Xiaodong Zhu, Suting Wang, Yuanming Zheng +5
Temporal Forgery Localization (TFL) aims to precisely identify manipulated segments in video and audio, offering strong interpretability for security and forensics. While recent St…
Improving Speech Enhancement by Cross- and Sub-band Processing with State Space Model
Jizhen Li, Weiping Tu, Yuhong Yang +3
Recently, the state space model (SSM) represented by Mamba has shown remarkable performance in long-term sequence modeling tasks, including speech enhancement. However, due to subs…
FreeCodec: A disentangled neural speech codec with fewer tokens
Youqiang Zheng, Weiping Tu, Yueteng Kang +5
Neural speech codecs have gained great attention for their outstanding reconstruction with discrete token representations. It is a crucial component in generative tasks such as spe…
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…
SimuSOE: A Simulated Snoring Dataset for Obstructive Sleep Apnea-Hypopnea Syndrome Evaluation during Wakefulness
Jie Lin, Xiuping Yang, Li Xiao +5
Obstructive Sleep Apnea-Hypopnea Syndrome (OSAHS) is a prevalent chronic breathing disorder caused by upper airway obstruction. Previous studies advanced OSAHS evaluation through m…
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…
Mandarin Lombard Flavor Classification
Qingmu Liu, Yuhong Yang, Baifeng Li +3
The Lombard effect refers to individuals' unconscious modulation of vocal effort in response to variations in the ambient noise levels, intending to enhance speech intelligibility.…
Exploring Sentence Type Effects on the Lombard Effect and Intelligibility Enhancement: A Comparative Study of Natural and Grid Sentences
Hongyang Chen, Yuhong Yang, Zhongyuan Wang +3
This study explores how sentence types affect the Lombard effect and intelligibility enhancement, focusing on comparisons between natural and grid sentences. Using the Lombard Chin…
EMALG: An Enhanced Mandarin Lombard Grid Corpus with Meaningful Sentences
Baifeng Li, Qingmu Liu, Yuhong Yang +3
This study investigates the Lombard effect, where individuals adapt their speech in noisy environments. We introduce an enhanced Mandarin Lombard grid (EMALG) corpus with meaningfu…
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…
A Snoring Sound Dataset for Body Position Recognition: Collection, Annotation, and Analysis
Li Xiao, Xiuping Yang, Xinhong Li +6
Obstructive Sleep Apnea-Hypopnea Syndrome (OSAHS) is a chronic breathing disorder caused by a blockage in the upper airways. Snoring is a prominent symptom of OSAHS, and previous s…
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…
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…