7 papers
Dual-View Predictive Diffusion: Lightweight Speech Enhancement via Spectrogram-Image Synergy
Ke Xue, Rongfei Fan, Kai Li +3
Diffusion models have recently set new benchmarks in Speech Enhancement (SE). However, most existing score-based models treat speech spectrograms merely as generic 2D images, apply…
Omni-directional attention mechanism based on Mamba for speech separation
Ke Xue, Chang Sun, Rongfei Fan +2
Mamba, a selective state-space model (SSM), has emerged as an efficient alternative to Transformers for speech modeling, enabling long-sequence processing with linear complexity. W…
From Coarse to Fine: Recursive Audio-Visual Semantic Enhancement for Speech Separation
Ke Xue, Rongfei Fan, Lixin +3
Audio-visual speech separation aims to isolate each speaker's clean voice from mixtures by leveraging visual cues such as lip movements and facial features. While visual informatio…
H+: An Efficient Similarity-Aware Aggregation for Byzantine Resilient Federated Learning
Shiyuan Zuo, Rongfei Fan, Cheng Zhan +3
Federated Learning (FL) enables decentralized model training without sharing raw data. However, it remains vulnerable to Byzantine attacks, which can compromise the aggregation of…
Semantic Communication in Dynamic Channel Scenarios: Collaborative Optimization of Dual-Pipeline Joint Source-Channel Coding and Personalized Federated Learning
Xingrun Yan, Shiyuan Zuo, Yifeng Lyu +2
Semantic communication is designed to tackle issues like bandwidth constraints and high latency in communication systems. However, in complex network topologies with multiple users…
On Theoretical Limits of Learning with Label Differential Privacy
Puning Zhao, Chuan Ma, Li Shen +2
Label differential privacy (DP) is designed for learning problems involving private labels and public features. While various methods have been proposed for learning under label DP…