Hideo Tsuru
Papers
1
Total Citations
3
H-Index
1
About
Hideo Tsuru is a researcher in robot audition and acoustic signal processing, with a focus on enabling machines to hear and interpret sound in dynamic environments. His key research areas include sound source localization, source separation, and adaptive acoustic modeling for robotic systems. Tsuru’s major contribution lies in developing online adaptation methods for Fourier series-based acoustic transfer function (TF) models, which capture how sound propagates from a source to a microphone array. His 2023 paper, "Online Adaptation of Fourier Series Based Acoustic Transfer Function Model to Improve Sound Source Localization and Separation," proposes a real-time approach to adjust these models as acoustic conditions change, significantly enhancing the robustness of robot audition in noisy or moving scenarios. While his work has garnered early citations, reflecting growing interest in adaptive auditory systems, Tsuru’s contributions are notable for bridging theoretical acoustic modeling with practical robotics, advancing the field’s ability to handle real-world auditory challenges. His research holds promise for applications in human-robot interaction and autonomous navigation, where reliable sound processing is critical.
Research Focus
Key Achievements
Top Papers
- 1