1 citations · 1 across the 5 of their papers we have counts for
5 papers
REDnet: Recursive Encoder and Decoder for Speech Separation under Unknown Number of Speakers and Variable Number of Microphones
Fulin Wu, Zhong-Qiu Wang
We propose (RED) for building a single deep neural network (DNN) model that can separate multi-speaker mixtures containing unknown numbers…
Cross-Talk Speech Reduction, by Separation, for Separation
Zhong-Qiu Wang, Samuele Cornell
In conversational speech separation and recognition tasks, close-talk microphones are typically attached to each speaker during training data collection to capture near-field, clos…
ARTT: Augmented Reverberant-Target Training for Unsupervised Monaural Speech Dereverberation
Siqi Song, Fulin Wu, Zhong-Qiu Wang
Due to the absence of clean reference signals and spatial cues, monaural unsupervised speech dereverberation is a challenging ill-posed inverse problem. To realize it, we propose a…
Unsupervised Multi-channel Speech Dereverberation via Diffusion
Yulun Wu, Zhongweiyang Xu, Jianchong Chen +2
We consider the problem of multi-channel single-speaker blind dereverberation, where multi-channel mixtures are used to recover the clean anechoic speech. To solve this problem, we…
ArrayDPS: Unsupervised Blind Speech Separation with a Diffusion Prior
Zhongweiyang Xu, Xulin Fan, Zhong-Qiu Wang +2
Blind Speech Separation (BSS) aims to separate multiple speech sources from audio mixtures recorded by a microphone array. The problem is challenging because it is a blind inverse…