Jizhen Li — accepted AI-conference papers

2025

FIRING-Net: A filtered feature recycling network for speech enhancement

Xinmeng Xu, Yiqun Zhang, Jizhen Li, Yuhong Yang, Yong Luo, Weiping Tu

ICLR 2025poster

Current deep neural networks for speech enhancement (SE) aim to minimize the distance between the output signal and the clean target by filtering out noise features from input features. However, when noise and speech components are highly similar, SE models struggle to learn effective discrimination…

Cited by 0 Source ↗PDF ↗Scholar ↗

2025

Improving Speech Enhancement by Cross- and Sub-band Processing with State Space Model

Jizhen Li, Weiping Tu, Yuhong Yang, Xinmeng Xu, Yiqun Zhang, Yanzhen Ren

ICASSP 2025accepted

Recently, the state space model (SSM) represented by Mamba has shown remarkable performance in long-term sequence modeling tasks, including speech enhancement. However, due to substantial differences in sub-band features, applying the same SSM to all sub-bands limits its inference capability. Additi…

Cited by 0 Source ↗Scholar ↗