Search Results for author:
Found 7 papers, 3 papers with code
MC-SEMamba: A Simple Multi-channel Extension of SEMamba
In particular, models like SEMamba have demonstrated the effectiveness of the Mamba architecture in single-channel speech enhancement.
Robust Audio-Visual Speech Enhancement: Correcting Misassignments in Complex Environments with Advanced Post-Processing
This paper addresses the prevalent issue of incorrect speech output in audio-visual speech enhancement (AVSE) systems, which is often caused by poor video quality and mismatched training and test data.
Leveraging Joint Spectral and Spatial Learning with MAMBA for Multichannel Speech Enhancement
In multichannel speech enhancement, effectively capturing spatial and spectral information across different microphones is crucial for noise reduction.
An Investigation of Incorporating Mamba for Speech Enhancement
This work aims to study a scalable state-space model (SSM), Mamba, for the speech enhancement (SE) task.
Ranked #4 on
Speech Enhancement
on VoiceBank + DEMAND
ElectrodeNet -- A Deep Learning Based Sound Coding Strategy for Cochlear Implants
ElectrodeNet, a deep learning based sound coding strategy for the cochlear implant (CI), is proposed to emulate the advanced combination encoder (ACE) strategy by replacing the conventional envelope detection using various artificial neural networks.
Perceptual Contrast Stretching on Target Feature for Speech Enhancement
Specifically, the contrast of target features is stretched based on perceptual importance, thereby improving the overall SE performance.
Ranked #14 on
Speech Enhancement
on VoiceBank + DEMAND
