6 papers
Frame-Wise Streaming for Efficient Learned Speech Compression
Yuhan Liu, Jun Xu, Fengxi Zhang +2
Speech compression is increasingly important in scenarios such as real-time and satellite communication. Neural codecs have shown superior Rate-Distortion (RD) performance over tra…
Benchmarking Neural Speech Compression from a Rate-Distortion Perspective
Jun Xu, Zhengxue Cheng, Fengxi Zhang +3
Learning-based speech compression has achieved promising low-bitrate performance, but many neural speech codecs still describe quantized latents with preset-rate discrete symbols o…
MeetBench-XL: Calibrated Multi-Dimensional Evaluation and Learned Dual-Policy Agents for Real-Time Meetings
Yuelin Hu, Jun Xu, Bingcong Lu +4
Enterprise meeting environments require AI assistants that handle diverse operational tasks, from rapid fact checking during live discussions to cross meeting analysis for strategi…
Lightweight High-Fidelity Low-Bitrate Talking Face Compression for 3D Video Conference
Jianglong Li, Jun Xu, Bingcong Lu +4
The demand for immersive and interactive communication has driven advancements in 3D video conferencing, yet achieving high-fidelity 3D talking face representation at low bitrates…
Rate-Aware Learned Speech Compression
Jun Xu, Zhengxue Cheng, Guangchuan Chi +3
The rapid rise of real-time communication and large language models has significantly increased the importance of speech compression. Deep learning-based neural speech codecs have…
Complexity-Oriented Per-shot Video Coding Optimization
Hongcheng Zhong, Jun Xu, Chen Zhu +2
Current per-shot encoding schemes aim to improve the compression efficiency by shot-level optimization. It splits a source video sequence into shots and imposes optimal sets of enc…