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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.
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.
IANS: Intelligibility-aware Null-steering Beamforming for Dual-Microphone Arrays
Beamforming techniques are popular in speech-related applications due to their effective spatial filtering capabilities.
Speech Enhancement Based on Cyclegan with Noise-informed Training
Herein, we investigate a potential limitation of the clean-to-noisy conversion part and propose a novel noise-informed training (NIT) approach to improve the performance of the original CycleGAN SE system.
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