1 citations · 3 across the 7 of their papers we have counts for
7 papers
Unified Diffusion Refinement for Multi-Channel Speech Enhancement and Separation
Zhongweiyang Xu, Ashutosh Pandey, Juan Azcarreta +4
We propose Uni-ArrayDPS, a novel diffusion-based refinement framework for unified multi-channel speech enhancement and separation. Existing methods for multi-channel speech enhance…
Learning Energy-based Variational Latent Prior for VAEs
Debottam Dutta, Chaitanya Amballa, Zhongweiyang Xu +2
Variational Auto-Encoders (VAEs) are known to generate blurry and inconsistent samples. One reason for this is the "prior hole" problem. A prior hole refers to regions that have hi…
Contrastive Diffusion Guidance for Spatial Inverse Problems
Sattwik Basu, Chaitanya Amballa, Zhongweiyang Xu +3
We consider a class of inverse problems characterized by forward operators that are partially specified, non-smooth, and non-differentiable. Although generative inverse solvers hav…
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…
Multi-Source Music Generation with Latent Diffusion
Zhongweiyang Xu, Debottam Dutta, Yu-Lin Wei +1
Most music generation models directly generate a single music mixture. To allow for more flexible and controllable generation, the Multi-Source Diffusion Model (MSDM) has been prop…
FoVNet: Configurable Field-of-View Speech Enhancement with Low Computation and Distortion for Smart Glasses
Zhongweiyang Xu, Ali Aroudi, Ke Tan +4
This paper presents a novel multi-channel speech enhancement approach, FoVNet, that enables highly efficient speech enhancement within a configurable field of view (FoV) of a smart…
Learning to Separate Voices by Spatial Regions
Zhongweiyang Xu, Romit Roy Choudhury
We consider the problem of audio voice separation for binaural applications, such as earphones and hearing aids. While today's neural networks perform remarkably well (separating $…