2 citations · 2 across the 9 of their papers we have counts for
9 papers · 1 filter
Forward Convolutive Prediction for Frame Online Monaural Speech Dereverberation Based on Kronecker Product Decomposition
Yujie Zhu, Jilu Jin, Xueqin Luo +5
Dereverberation has long been a crucial research topic in speech processing, aiming to alleviate the adverse effects of reverberation in voice communication and speech interaction…
Mixture to Beamformed Mixture: Leveraging Beamformed Mixture as Weak-Supervision for Speech Enhancement and Noise-Robust ASR
Zhong-Qiu Wang, Ruizhe Pang
In multi-channel speech enhancement and robust automatic speech recognition (ASR), beamforming can typically improve the signal-to-noise ratio (SNR) of the target speaker and produ…
AuralNet: Hierarchical Attention-based 3D Binaural Localization of Overlapping Speakers
Linya Fu, Yu Liu, Zhijie Liu +4
We propose AuralNet, a novel 3D multi-source binaural sound source localization approach that localizes overlapping sources in both azimuth and elevation without prior knowledge of…
30+ Years of Source Separation Research: Achievements and Future Challenges
Shoko Araki, Nobutaka Ito, Reinhold Haeb-Umbach +3
Source separation (SS) of acoustic signals is a research field that emerged in the mid-1990s and has flourished ever since. On the occasion of ICASSP's 50th anniversary, we review…
ctPuLSE: Close-Talk, and Pseudo-Label Based Far-Field, Speech Enhancement
Zhong-Qiu Wang
The current dominant approach for neural speech enhancement is via purely-supervised deep learning on simulated pairs of far-field noisy-reverberant speech (i.e., mixtures) and cle…
Cross-Talk Reduction
Zhong-Qiu Wang, Anurag Kumar, Shinji Watanabe
While far-field multi-talker mixtures are recorded, each speaker can wear a close-talk microphone so that close-talk mixtures can be recorded at the same time. Although each close-…
SuperM2M: Supervised and Mixture-to-Mixture Co-Learning for Speech Enhancement and Noise-Robust ASR
Zhong-Qiu Wang
The current dominant approach for neural speech enhancement is based on supervised learning by using simulated training data. The trained models, however, often exhibit limited gen…
Mixture to Mixture: Leveraging Close-talk Mixtures as Weak-supervision for Speech Separation
Zhong-Qiu Wang
We propose mixture to mixture (M2M) training, a weakly-supervised neural speech separation algorithm that leverages close-talk mixtures as a weak supervision for training discrimin…
USDnet: Unsupervised Speech Dereverberation via Neural Forward Filtering
Zhong-Qiu Wang
In reverberant conditions with a single speaker, each far-field microphone records a reverberant version of the same speaker signal at a different location. In over-determined cond…