Xinguo Li
Papers
1
Total Citations
2
H-Index
1
About
Xinguo Li is a researcher whose work lies at the intersection of robot audition and environmental sound understanding. His primary research focuses on developing robust, real-time systems for sound event detection and recognition—enabling robots to perceive and interpret their acoustic surroundings as naturally as humans do. In his notable 2013 paper, "On-line sound event detection and recognition based on adaptive background model for robot audition," Li tackled the challenging problem of detecting and recognizing sound events in noisy, dynamic indoor environments. He proposed an adaptive background model that allows a robot to continuously update its understanding of ambient noise, thereby improving the accuracy of sound event detection even as the acoustic environment changes. This work, which has garnered 2 citations, is foundational for creating more responsive and context-aware robotic systems. By advancing how robots "listen," Li contributes to making human-robot interaction more intuitive and seamless, with potential applications in assistive robotics, smart environments, and autonomous navigation.
Research Focus
Key Achievements
Top Papers
- 1