104 citations · 200 across the 24 of their papers we have counts for
13 papers · 1 filter
UNSSOR: Unsupervised Neural Speech Separation by Leveraging Over-determined Training Mixtures
Zhong-Qiu Wang, Shinji Watanabe
In reverberant conditions with multiple concurrent speakers, each microphone acquires a mixture signal of multiple speakers at a different location. In over-determined conditions w…
TF-GridNet: Making Time-Frequency Domain Models Great Again for Monaural Speaker Separation
Zhong-Qiu Wang, Samuele Cornell, Shukjae Choi +3
We propose TF-GridNet, a novel multi-path deep neural network (DNN) operating in the time-frequency (T-F) domain, for monaural talker-independent speaker separation in anechoic con…
STFT-Domain Neural Speech Enhancement with Very Low Algorithmic Latency
Zhong-Qiu Wang, Gordon Wichern, Shinji Watanabe +1
Deep learning based speech enhancement in the short-time Fourier transform (STFT) domain typically uses a large window length such as 32 ms. A larger window can lead to higher freq…
Improving Frame-Online Neural Speech Enhancement with Overlapped-Frame Prediction
Zhong-Qiu Wang, Shinji Watanabe
Frame-online speech enhancement systems in the short-time Fourier transform (STFT) domain usually have an algorithmic latency equal to the window size due to the use of overlap-add…
Leveraging Low-Distortion Target Estimates for Improved Speech Enhancement
Zhong-Qiu Wang, Gordon Wichern, Jonathan Le Roux
A promising approach for multi-microphone speech separation involves two deep neural networks (DNN), where the predicted target speech from the first DNN is used to compute signal…
On The Compensation Between Magnitude and Phase in Speech Separation
Zhong-Qiu Wang, Gordon Wichern, Jonathan Le Roux
Deep neural network (DNN) based end-to-end optimization in the complex time-frequency (T-F) domain or time domain has shown considerable potential in monaural speech separation. Ma…
Convolutive Prediction for Reverberant Speech Separation
Zhong-Qiu Wang, Gordon Wichern, Jonathan Le Roux
We investigate the effectiveness of convolutive prediction, a novel formulation of linear prediction for speech dereverberation, for speaker separation in reverberant conditions. T…
Convolutive Prediction for Monaural Speech Dereverberation and Noisy-Reverberant Speaker Separation
Zhong-Qiu Wang, Gordon Wichern, Jonathan Le Roux
A promising approach for speech dereverberation is based on supervised learning, where a deep neural network (DNN) is trained to predict the direct sound from noisy-reverberant spe…
Localization Based Sequential Grouping for Continuous Speech Separation
Zhong-Qiu Wang, DeLiang Wang
This study investigates robust speaker localization for con-tinuous speech separation and speaker diarization, where we use speaker directions to group non-contiguous segments of t…
Multi-microphone Complex Spectral Mapping for Utterance-wise and Continuous Speech Separation
Zhong-Qiu Wang, Peidong Wang, DeLiang Wang
We propose multi-microphone complex spectral mapping, a simple way of applying deep learning for time-varying non-linear beamforming, for speaker separation in reverberant conditio…
Sequential Multi-Frame Neural Beamforming for Speech Separation and Enhancement
Zhong-Qiu Wang, Hakan Erdogan, Scott Wisdom +5
This work introduces sequential neural beamforming, which alternates between neural network based spectral separation and beamforming based spatial separation. Our neural networks…
Deep Learning Based Phase Reconstruction for Speaker Separation: A Trigonometric Perspective
Zhong-Qiu Wang, Ke Tan, DeLiang Wang
This study investigates phase reconstruction for deep learning based monaural talker-independent speaker separation in the short-time Fourier transform (STFT) domain. The key obser…
End-to-End Speech Separation with Unfolded Iterative Phase Reconstruction
Zhong-Qiu Wang, Jonathan Le Roux, DeLiang Wang +1
This paper proposes an end-to-end approach for single-channel speaker-independent multi-speaker speech separation, where time-frequency (T-F) masking, the short-time Fourier transf…