14 citations · 111 across the 35 of their papers we have counts for
35 papers
Scalable Neural Vocoder from Range-Null Space Decomposition
Andong Li, Tong Lei, Zhihang Sun +4
Although deep neural networks have facilitated significant progress of neural vocoders in recent years, they usually suffer from intrinsic challenges like opaque modeling, inflexib…
SLD-L2S: Hierarchical Subspace Latent Diffusion for High-Fidelity Lip to Speech Synthesis
Yifan Liang, Andong Li, Kang Yang +5
Although lip-to-speech synthesis (L2S) has achieved significant progress in recent years, current state-of-the-art methods typically rely on intermediate representations such as me…
GOMPSNR: Reflourish the Signal-to-Noise Ratio Metric for Audio Generation Tasks
Lingling Dai, Andong Li, Cheng Chi +3
In the field of audio generation, signal-to-noise ratio (SNR) has long served as an objective metric for evaluating audio quality. Nevertheless, recent studies have shown that SNR…
NaturalL2S: End-to-End High-quality Multispeaker Lip-to-Speech Synthesis with Differential Digital Signal Processing
Yifan Liang, Fangkun Liu, Andong Li +2
Recent advancements in visual speech recognition (VSR) have promoted progress in lip-to-speech synthesis, where pre-trained VSR models enhance the intelligibility of synthesized sp…
Neural Vocoders as Speech Enhancers
Andong Li, Zhihang Sun, Fengyuan Hao +2
Speech enhancement (SE) and neural vocoding are traditionally viewed as separate tasks. In this work, we observe them under a common thread: the rank behavior of these processes. T…
BSDB-Net: Band-Split Dual-Branch Network with Selective State Spaces Mechanism for Monaural Speech Enhancement
Cunhang Fan, Enrui Liu, Andong Li +5
Although the complex spectrum-based speech enhancement(SE) methods have achieved significant performance, coupling amplitude and phase can lead to a compensation effect, where ampl…
SMRU: Split-and-Merge Recurrent-based UNet for Acoustic Echo Cancellation and Noise Suppression
Zhihang Sun, Andong Li, Rilin Chen +4
The proliferation of deep neural networks has spawned the rapid development of acoustic echo cancellation and noise suppression, and plenty of prior arts have been proposed, which…
Gesper: A Restoration-Enhancement Framework for General Speech Reconstruction
Wenzhe Liu, Yupeng Shi, Jun Chen +5
This paper describes a real-time General Speech Reconstruction (Gesper) system submitted to the ICASSP 2023 Speech Signal Improvement (SSI) Challenge. This novel proposed system is…
TaylorBeamixer: Learning Taylor-Inspired All-Neural Multi-Channel Speech Enhancement from Beam-Space Dictionary Perspective
Andong Li, Guochen Yu, Wenzhe Liu +2
Despite the promising performance of existing frame-wise all-neural beamformers in the speech enhancement field, it remains unclear what the underlying mechanism exists. In this pa…
A General Unfolding Speech Enhancement Method Motivated by Taylor's Theorem
Andong Li, Guochen Yu, Chengshi Zheng +2
While deep neural networks have facilitated significant advancements in the field of speech enhancement, most existing methods are developed following either empirical or relativel…
TMGAN-PLC: Audio Packet Loss Concealment using Temporal Memory Generative Adversarial Network
Yuansheng Guan, Guochen Yu, Andong Li +2
Real-time communications in packet-switched networks have become widely used in daily communication, while they inevitably suffer from network delays and data losses in constrained…
Taylor, Can You Hear Me Now? A Taylor-Unfolding Framework for Monaural Speech Enhancement
Andong Li, Shan You, Guochen Yu +2
While the deep learning techniques promote the rapid development of the speech enhancement (SE) community, most schemes only pursue the performance in a black-box manner and lack a…
TaylorBeamformer: Learning All-Neural Beamformer for Multi-Channel Speech Enhancement from Taylor's Approximation Theory
Andong Li, Guochen Yu, Chengshi Zheng +1
While existing end-to-end beamformers achieve impressive performance in various front-end speech processing tasks, they usually encapsulate the whole process into a black box and t…
MDNet: Learning Monaural Speech Enhancement from Deep Prior Gradient
Andong Li, Chengshi Zheng, Ziyang Zhang +1
While traditional statistical signal processing model-based methods can derive the optimal estimators relying on specific statistical assumptions, current learning-based methods fu…
Optimizing Shoulder to Shoulder: A Coordinated Sub-Band Fusion Model for Real-Time Full-Band Speech Enhancement
Guochen Yu, Andong Li, Wenzhe Liu +3
Due to the high computational complexity to model more frequency bands, it is still intractable to conduct real-time full-band speech enhancement based on deep neural networks. Rec…
Low-latency Monaural Speech Enhancement with Deep Filter-bank Equalizer
Chengshi Zheng, Wenzhe Liu, Andong Li +2
It is highly desirable that speech enhancement algorithms can achieve good performance while keeping low latency for many applications, such as digital hearing aids, acoustically t…
A Neural Beam Filter for Real-time Multi-channel Speech Enhancement
Wenzhe Liu, Andong Li, Chengshi Zheng +1
Most deep learning-based multi-channel speech enhancement methods focus on designing a set of beamforming coefficients to directly filter the low signal-to-noise ratio signals rece…
DBT-Net: Dual-branch federative magnitude and phase estimation with attention-in-attention transformer for monaural speech enhancement
Guochen Yu, Andong Li, Hui Wang +3
The decoupling-style concept begins to ignite in the speech enhancement area, which decouples the original complex spectrum estimation task into multiple easier sub-tasks i.e., mag…
A deep complex multi-frame filtering network for stereophonic acoustic echo cancellation
Linjuan Cheng, Chengshi Zheng, Andong Li +3
In hands-free communication system, the coupling between loudspeaker and microphone generates echo signal, which can severely influence the quality of communication. Meanwhile, var…
A Novel TSK Fuzzy System Incorporating Multi-view Collaborative Transfer Learning for Personalized Epileptic EEG Detection
Andong Li, Zhaohong Deng, Qiongdan Lou
In clinical practice, electroencephalography (EEG) plays an important role in the diagnosis of epilepsy. EEG-based computer-aided diagnosis of epilepsy can greatly improve the ac-c…
Dual-branch Attention-In-Attention Transformer for single-channel speech enhancement
Guochen Yu, Andong Li, Chengshi Zheng +3
Curriculum learning begins to thrive in the speech enhancement area, which decouples the original spectrum estimation task into multiple easier sub-tasks to achieve better performa…
Embedding and Beamforming: All-neural Causal Beamformer for Multichannel Speech Enhancement
Andong Li, Wenzhe Liu, Chengshi Zheng +1
The spatial covariance matrix has been considered to be significant for beamformers. Standing upon the intersection of traditional beamformers and deep neural networks, we propose…
Joint magnitude estimation and phase recovery using Cycle-in-Cycle GAN for non-parallel speech enhancement
Guochen Yu, Andong Li, Yutian Wang +3
For the lack of adequate paired noisy-clean speech corpus in many real scenarios, non-parallel training is a promising task for DNN-based speech enhancement methods. However, becau…
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.…