80 citations · 106 across the 17 of their papers we have counts for
6 papers · 1 filter
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…
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…
Improving RNN Transducer With Target Speaker Extraction and Neural Uncertainty Estimation
Jiatong Shi, Chunlei Zhang, Chao Weng +3
Target-speaker speech recognition aims to recognize target-speaker speech from noisy environments with background noise and interfering speakers. This work presents a joint framewo…
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…
A comprehensive study of speech separation: spectrogram vs waveform separation
Fahimeh Bahmaninezhad, Jian Wu, Rongzhi Gu +4
Speech separation has been studied widely for single-channel close-talk microphone recordings over the past few years; developed solutions are mostly in frequency-domain. Recently,…
Deep Extractor Network for Target Speaker Recovery From Single Channel Speech Mixtures
Jun Wang, Jie Chen, Dan Su +4
Speaker-aware source separation methods are promising workarounds for major difficulties such as arbitrary source permutation and unknown number of sources. However, it remains cha…