7 papers
Audiovisual joint learning for end-to-end hearing aids
You-Jin Li, Yu Tsao, Borching Su +2
Speech understanding in noise remains challenging for hearing-aid users, particularly in the presence of competing speakers. Conventional hearing aids typically perform speech enha…
HAMMER: Harmonic-Aware Parallel Context Modeling and Discriminator-Free Perceptual Optimization for Speech Enhancement
Shang-Fu Chen, Szu-Wei Fu, Sung-Feng Huang +3
Recent speech enhancement systems combine self-attention and Mamba to capture global interactions and long-range dependencies. Yet these hybrids usually operate as sequence mixers…
Navigating Speech Enhancement for Real-Time MRI: A Systematic Assessment of Signal Quality, Source Preservation, and Downstream Tasks
Huang-Cheng Chou, Sean Foley, Haley Hsu +10
Audio recorded during real-time magnetic resonance imaging (rtMRI) is heavily contaminated by scanner noise, but it remains unclear whether general-purpose speech enhancement impro…
RT-SEMamba: Real-Time Speech Enhancement Mamba via Progressive Knowledge Distillation
Rong Chao, Sung-Feng Huang, Moreno La Quatra +4
We present RT-SEMamba, a fully causal speech enhancement (SE) model built upon causal time-frequency Mamba blocks. Unlike Transformer-based architectures that rely on a growing key…
One Model, Many Latencies: Universal Speech Enhancement for Diverse Real-Time Applications
Szu-Wei Fu, Rong Chao, Xuesong Yang +4
Different real-time speech applications impose distinct latency budgets, often requiring separately trained enhancement models for each scenario. In this paper, we propose a one-fo…
Rethinking Training Targets, Architectures and Data Quality for Universal Speech Enhancement
Szu-Wei Fu, Rong Chao, Xuesong Yang +6
Universal Speech Enhancement (USE) aims to restore speech quality under diverse degradation conditions while preserving signal fidelity. Despite recent progress, key challenges in…
Tracking Listener Attention: Gaze-Guided Audio-Visual Speech Enhancement Framework
Hsiang-Cheng Yang, You-Jin Li, Rong Chao +3
This paper presents a Gaze-Guided Audio-Visual Speech Enhancement (GG-AVSE) framework to address the cocktail party problem. A major challenge in conventional AVSE is identifying t…