Ting Fan
Papers
2
Total Citations
18
H-Index
2
About
Ting Fan is a researcher in robot audition and human-robot interaction (HRI), specializing in making robots understand speech and sound in noisy, real-world environments. His work addresses the critical challenge of noise-robustness, enabling robots to reliably detect and interpret audio cues despite background interference. In his most cited paper (16 citations), Fan introduced an audio-visual keyword spotting system that uses adaptive decision fusion to improve keyword identification under noisy conditions, directly enhancing the naturalness and reliability of voice-based HRI. He also developed an on-line sound event detection and recognition method using an adaptive background model, allowing robots to dynamically filter varying indoor noises and recognize non-speech audio events. These contributions are foundational for building more perceptive and responsive robotic systems that can operate in everyday, acoustically complex settings. Fan’s work bridges signal processing, machine learning, and robotics, offering practical solutions for robust robot audition.
Research Focus
Key Achievements
Top Papers
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