111 citations · 781 across the 43 of their papers we have counts for
44 papers
Deep Neural Mel-Subband Beamformer for In-car Speech Separation
Vinay Kothapally, Yong Xu, Meng Yu +2
While current deep learning (DL)-based beamforming techniques have been proved effective in speech separation, they are often designed to process narrow-band (NB) frequencies indep…
Neural Sound Field Decomposition with Super-resolution of Sound Direction
Qiuqiang Kong, Shilei Liu, Junjie Shi +5
Sound field decomposition predicts waveforms in arbitrary directions using signals from a limited number of microphones as inputs. Sound field decomposition is fundamental to downs…
NeuralEcho: A Self-Attentive Recurrent Neural Network For Unified Acoustic Echo Suppression And Speech Enhancement
Meng Yu, Yong Xu, Chunlei Zhang +2
Acoustic echo cancellation (AEC) plays an important role in the full-duplex speech communication as well as the front-end speech enhancement for recognition in the conditions when…
EEND-SS: Joint End-to-End Neural Speaker Diarization and Speech Separation for Flexible Number of Speakers
Soumi Maiti, Yushi Ueda, Shinji Watanabe +4
In this paper, we present a novel framework that jointly performs three tasks: speaker diarization, speech separation, and speaker counting. Our proposed framework integrates speak…
Joint Neural AEC and Beamforming with Double-Talk Detection
Vinay Kothapally, Yong Xu, Meng Yu +2
Acoustic echo cancellation (AEC) in full-duplex communication systems eliminates acoustic feedback. However, nonlinear distortions induced by audio devices, background noise, rever…
MIMO Self-attentive RNN Beamformer for Multi-speaker Speech Separation
Xiyun Li, Yong Xu, Meng Yu +4
Recently, our proposed recurrent neural network (RNN) based all deep learning minimum variance distortionless response (ADL-MVDR) beamformer method yielded superior performance ove…
MetricNet: Towards Improved Modeling For Non-Intrusive Speech Quality Assessment
Meng Yu, Chunlei Zhang, Yong Xu +2
The objective speech quality assessment is usually conducted by comparing received speech signal with its clean reference, while human beings are capable of evaluating the speech q…
TeCANet: Temporal-Contextual Attention Network for Environment-Aware Speech Dereverberation
Helin Wang, Bo Wu, Lianwu Chen +7
In this paper, we exploit the effective way to leverage contextual information to improve the speech dereverberation performance in real-world reverberant environments. We propose…
Generalized Spatio-Temporal RNN Beamformer for Target Speech Separation
Yong Xu, Zhuohuang Zhang, Meng Yu +2
Although the conventional mask-based minimum variance distortionless response (MVDR) could reduce the non-linear distortion, the residual noise level of the MVDR separated speech i…
Multi-channel Multi-frame ADL-MVDR for Target Speech Separation
Zhuohuang Zhang, Yong Xu, Meng Yu +4
Many purely neural network based speech separation approaches have been proposed to improve objective assessment scores, but they often introduce nonlinear distortions that are har…
WPD++: An Improved Neural Beamformer for Simultaneous Speech Separation and Dereverberation
Zhaoheng Ni, Yong Xu, Meng Yu +4
This paper aims at eliminating the interfering speakers' speech, additive noise, and reverberation from the noisy multi-talker speech mixture that benefits automatic speech recogni…
Directional ASR: A New Paradigm for E2E Multi-Speaker Speech Recognition with Source Localization
Aswin Shanmugam Subramanian, Chao Weng, Shinji Watanabe +4
This paper proposes a new paradigm for handling far-field multi-speaker data in an end-to-end neural network manner, called directional automatic speech recognition (D-ASR), which…
An Overview of Deep-Learning-Based Audio-Visual Speech Enhancement and Separation
Daniel Michelsanti, Zheng-Hua Tan, Shi-Xiong Zhang +4
Speech enhancement and speech separation are two related tasks, whose purpose is to extract either one or more target speech signals, respectively, from a mixture of sounds generat…
ADL-MVDR: All deep learning MVDR beamformer for target speech separation
Zhuohuang Zhang, Yong Xu, Meng Yu +3
Speech separation algorithms are often used to separate the target speech from other interfering sources. However, purely neural network based speech separation systems often cause…
Distortionless Multi-Channel Target Speech Enhancement for Overlapped Speech Recognition
Bo Wu, Meng Yu, Lianwu Chen +4
Speech enhancement techniques based on deep learning have brought significant improvement on speech quality and intelligibility. Nevertheless, a large gain in speech quality measur…
Neural Spatio-Temporal Beamformer for Target Speech Separation
Yong Xu, Meng Yu, Shi-Xiong Zhang +4
Purely neural network (NN) based speech separation and enhancement methods, although can achieve good objective scores, inevitably cause nonlinear speech distortions that are harmf…
Enhancing End-to-End Multi-channel Speech Separation via Spatial Feature Learning
Rongzhi Gu, Shi-Xiong Zhang, Lianwu Chen +5
Hand-crafted spatial features (e.g., inter-channel phase difference, IPD) play a fundamental role in recent deep learning based multi-channel speech separation (MCSS) methods. Howe…
Multi-modal Multi-channel Target Speech Separation
Rongzhi Gu, Shi-Xiong Zhang, Yong Xu +3
Target speech separation refers to extracting a target speaker's voice from an overlapped audio of simultaneous talkers. Previously the use of visual modality for target speech sep…
Self-supervised learning for audio-visual speaker diarization
Yifan Ding, Yong Xu, Shi-Xiong Zhang +2
Speaker diarization, which is to find the speech segments of specific speakers, has been widely used in human-centered applications such as video conferences or human-computer inte…
A Unified Framework for Speech Separation
Fahimeh Bahmaninezhad, Shi-Xiong Zhang, Yong Xu +3
Speech separation refers to extracting each individual speech source in a given mixed signal. Recent advancements in speech separation and ongoing research in this area, have made…
Sound Event Detection of Weakly Labelled Data with CNN-Transformer and Automatic Threshold Optimization
Qiuqiang Kong, Yong Xu, Wenwu Wang +1
Sound event detection (SED) is a task to detect sound events in an audio recording. One challenge of the SED task is that many datasets such as the Detection and Classification of…
Audio-Visual Speech Separation and Dereverberation with a Two-Stage Multimodal Network
Ke Tan, Yong Xu, Shi-Xiong Zhang +2
Background noise, interfering speech and room reverberation frequently distort target speech in real listening environments. In this study, we address joint speech separation and d…
Single-Channel Signal Separation and Deconvolution with Generative Adversarial Networks
Qiuqiang Kong, Yong Xu, Wenwu Wang +2
Single-channel signal separation and deconvolution aims to separate and deconvolve individual sources from a single-channel mixture and is a challenging problem in which no prior k…
End-to-End Multi-Channel Speech Separation
Rongzhi Gu, Jian Wu, Shi-Xiong Zhang +6
The end-to-end approach for single-channel speech separation has been studied recently and shown promising results. This paper extended the previous approach and proposed a new end…