Tamotsu Mitamura
Papers
1
Total Citations
2
H-Index
1
About
Tamotsu Mitamura is a researcher whose work sits at the intersection of robotics, artificial intelligence, and acoustic signal processing. His key research areas include autonomous robotic systems, deep learning, and acoustic event recognition—particularly for challenging, real-world environments. Mitamura’s major contribution is the development of a novel deep learning-based approach for classifying and recognizing acoustic events in noisy settings, designed specifically for multiple autonomous robots. This work is critical for disaster response robotics, where the ability to detect and interpret sounds—such as alarms, calls for help, or structural failures—can dramatically improve mission effectiveness. His most cited paper, “An Acoustic Events Recognition for Robotic Systems Based on a Deep Learning Method” (2015), lays the foundation for this approach, demonstrating how deep neural networks can be trained to filter out noise and accurately identify key acoustic cues. While his citation count is modest, the practical implications of his research are significant, offering a pathway toward more perceptive and autonomous robotic systems capable of operating in chaotic, real-world scenarios. Mitamura’s work stands as a valuable contribution to the growing field of robot audition and intelligent sensing.
Research Focus
Key Achievements
Top Papers
- 1