Ju-man Song
Papers
2
Total Citations
4
H-Index
2
About
Ju-man Song is a researcher specializing in acoustic signal processing, sensor fusion, and intelligent robotic systems, with a particular focus on sound source localization in challenging environments. His work addresses critical challenges in both domestic and underwater robotics, where traditional sensors like cameras and laser scanners are limited by environmental noise or medium constraints. Song’s most notable contribution is the development of a real-time sound source localization system for robotic vacuum cleaners using a microphone array, published in 2024. This system overcomes the significant problem of strong ego-noise generated by the robot itself, enabling more accurate voice command recognition and user tracking—a key advancement for smart home appliances. In earlier work (2013), Song proposed a direction and location estimating algorithm for underwater sound sources using just two hydrophones, demonstrating that valuable spatial information can be extracted from acoustic signals in underwater environments where visual sensors fail. While his citation counts are currently modest (2 each), these papers represent foundational steps in practical, noise-robust acoustic sensing for real-world robots. Song’s research bridges the gap between theoretical acoustics and deployable robotic systems, making him a promising contributor to the fields of human-robot interaction and autonomous underwater vehicles.
Research Focus
Key Achievements
Top Papers
- 1
- 2