5 papers
LORT: Locally Refined Convolution and Taylor Transformer for Monaural Speech Enhancement
Junyu Wang, Zizhen Lin, Tianrui Wang +3
Achieving superior enhancement performance while maintaining a low parameter count and computational complexity remains a challenge in the field of speech enhancement. In this pape…
PrimeK-Net: Multi-scale Spectral Learning via Group Prime-Kernel Convolutional Neural Networks for Single Channel Speech Enhancement
Zizhen Lin, Junyu Wang, Ruili Li +2
Single-channel speech enhancement is a challenging ill-posed problem focused on estimating clean speech from degraded signals. Existing studies have demonstrated the competitive pe…
Mamba-SEUNet: Mamba UNet for Monaural Speech Enhancement
Junyu Wang, Zizhen Lin, Tianrui Wang +3
In recent speech enhancement (SE) research, transformer and its variants have emerged as the predominant methodologies. However, the quadratic complexity of the self-attention mech…
Dense-TSNet: Dense Connected Two-Stage Structure for Ultra-Lightweight Speech Enhancement
Zizhen Lin, Yuanle Li, Junyu Wang +1
Speech enhancement aims to improve speech quality and intelligibility in noisy environments. Recent advancements have concentrated on deep neural networks, particularly employing t…
MUSE: Flexible Voiceprint Receptive Fields and Multi-Path Fusion Enhanced Taylor Transformer for U-Net-based Speech Enhancement
Zizhen Lin, Xiaoting Chen, Junyu Wang
Achieving a balance between lightweight design and high performance remains a challenging task for speech enhancement. In this paper, we introduce Multi-path Enhanced Taylor (MET)…