5 papers · 1 filter
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…
Pay More Attention To Audio: Mitigating Imbalance of Cross-Modal Attention in Large Audio Language Models
Junyu Wang, Ziyang Ma, Zhengding Luo +4
Large Audio-Language Models (LALMs) often suffer from audio-textual attention imbalance, prioritizing text over acoustic information, particularly in the multi-modal fusion layers…
ASDA: Audio Spectrogram Differential Attention Mechanism for Self-Supervised Representation Learning
Junyu Wang, Tianrui Wang, Meng Ge +2
In recent advancements in audio self-supervised representation learning, the standard Transformer architecture has emerged as the predominant approach, yet its attention mechanism…
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…