5 citations · 8 across the 6 of their papers we have counts for
7 papers
Towards Low-distortion Multi-channel Speech Enhancement: The ESPNet-SE Submission to The L3DAS22 Challenge
Yen-Ju Lu, Samuele Cornell, Xuankai Chang +5
This paper describes our submission to the L3DAS22 Challenge Task 1, which consists of speech enhancement with 3D Ambisonic microphones. The core of our approach combines Deep Neur…
Combining Spatial Clustering with LSTM Speech Models for Multichannel Speech Enhancement
Felix Grezes, Zhaoheng Ni, Viet Anh Trinh +1
Recurrent neural networks using the LSTM architecture can achieve significant single-channel noise reduction. It is not obvious, however, how to apply them to multi-channel inputs…
Improved MVDR Beamforming Using LSTM Speech Models to Clean Spatial Clustering Masks
Zhaoheng Ni, Felix Grezes, Viet Anh Trinh +1
Spatial clustering techniques can achieve significant multi-channel noise reduction across relatively arbitrary microphone configurations, but have difficulty incorporating a detai…
Enhancement of Spatial Clustering-Based Time-Frequency Masks using LSTM Neural Networks
Felix Grezes, Zhaoheng Ni, Viet Anh Trinh +1
Recent works have shown that Deep Recurrent Neural Networks using the LSTM architecture can achieve strong single-channel speech enhancement by estimating time-frequency masks. How…
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…
Onssen: an open-source speech separation and enhancement library
Zhaoheng Ni, Michael I Mandel
Speech separation is an essential task for multi-talker speech recognition. Recently many deep learning approaches are proposed and have been constantly refreshing the state-of-the…