Masayuki Takigahira
Papers
2
Total Citations
6
H-Index
2
About
Masayuki Takigahira is a researcher in robot audition, specializing in microphone array signal processing and adaptive acoustic modeling. His work centers on the critical challenge of enabling robots to robustly localize and separate sound sources in dynamic, real-world environments—a fundamental capability for human-robot interaction. Takigahira’s major contribution lies in developing fully-online, always-adaptive methods for acoustic transfer function (TF) models. Unlike traditional static models, his Fourier series-based approach allows the system to continuously update its representation of signal propagation between microphones and sound sources, even as the robot or its environment changes. This innovation directly improves the accuracy and reliability of sound source localization and separation, moving robot audition from controlled labs to practical applications. His most-cited papers, including “Online Adaptation of Fourier Series Based Acoustic Transfer Function Model” (2023, 3 citations) and “Fully-Online Always-Adaptation of Transfer Functions” (2021, 3 citations), lay the groundwork for systems that can “always listen and learn,” a paradigm shift for autonomous auditory perception. Takigahira’s work is essential reading for students and engineers building the next generation of hearing-capable robots.
Research Focus
Key Achievements
Top Papers
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