Daisuke Kimoto

Papers

Total Citations

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About

Daisuke Kimoto’s research lies at the intersection of robotics, auditory perception, and Bayesian inference, with a focus on enabling machines to actively localize sound sources in dynamic environments. His most cited work, “Active Sound Source Localization by Pinnae with Recursive Bayesian Estimation” (2017), draws inspiration from biological hearing—specifically how animals use two ears and external pinnae to pinpoint sounds. Kimoto’s key contribution is a robotic system that employs two microphones with active external ear structures, coupled with recursive Bayesian estimation, to dynamically and accurately track sound sources. This approach mimics the adaptive, exploratory movements of animal pinnae, allowing the robot to resolve spatial ambiguities and improve localization over time. While his citation count (5 for this paper) reflects a niche but growing field, the work is notable for its innovative integration of active sensing with probabilistic filtering, offering a pathway toward more autonomous, perceptive robots. Kimoto’s research has implications for human-robot interaction, assistive technologies, and bio-inspired sensor design, establishing him as a thoughtful contributor to the emerging domain of active auditory robotics.