12 citations · 12 across the 12 of their papers we have counts for
5 papers · 1 filter
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…
ArrayDPS-Refine: Generative Refinement of Discriminative Multi-Channel Speech Enhancement
Zhongweiyang Xu, Ashutosh Pandey, Juan Azcarreta +3
Multi-channel speech enhancement aims to recover clean speech from noisy multi-channel recordings. Most deep learning methods employ discriminative training, which can lead to non-…
Improving Resource-Efficient Speech Enhancement via Neural Differentiable DSP Vocoder Refinement
Heitor R. Guimarães, Ke Tan, Juan Azcarreta +4
Deploying speech enhancement (SE) systems in wearable devices, such as smart glasses, is challenging due to the limited computational resources on the device. Although deep learnin…
Modulating State Space Model with SlowFast Framework for Compute-Efficient Ultra Low-Latency Speech Enhancement
Longbiao Cheng, Ashutosh Pandey, Buye Xu +3
Deep learning-based speech enhancement (SE) methods often face significant computational challenges when needing to meet low-latency requirements because of the increased number of…
Dynamic Gated Recurrent Neural Network for Compute-efficient Speech Enhancement
Longbiao Cheng, Ashutosh Pandey, Buye Xu +2
This paper introduces a new Dynamic Gated Recurrent Neural Network (DG-RNN) for compute-efficient speech enhancement models running on resource-constrained hardware platforms. It l…