Papers
9
Total Citations
55
H-Index
4
About
Kenji Nishida is a researcher whose work spans robot audition, environmental sound processing, and autonomous robot navigation. His most significant contributions center on developing advanced auditory scene analysis techniques for real-world robotic applications, with particular emphasis on environmental sound segmentation. His pioneering work applying Mask U-Net architectures to environmental sound segmentation — published in 2019 and refined in 2020 — has garnered a combined 25 citations, establishing him as a notable voice in machine listening for human-robot interaction. Nishida has also made meaningful contributions to sound source localization and separation, including beamforming techniques for surface sound sources and microphone array-based localization evaluated in challenging outdoor drone scenarios. His earlier work in mobile robotics, particularly his research into hippocampal place cell modeling using self-organizing maps and reinforcement learning for robot navigation (2001, 10 citations), demonstrates a long-standing interest in biologically inspired approaches to autonomous systems. More recently, he has extended his reach into edge computing solutions for socially assistive robotics. Nishida's career reflects a consistent commitment to bridging acoustic signal processing and intelligent robotics, making his work valuable reading for researchers in auditory AI and human-robot interaction.
Research Focus
Key Achievements
Top Papers
- 1Sound event aware environmental sound segmentation with Mask U-Net14 citations · 2020
- 2Environmental sound segmentation utilizing Mask U-Net11 citations · 2019
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- 5Constructing a map of place cells for mobile robot navigation4 citations · 2004
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