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About

Tadashi Enomoto’s research lies at the intersection of mobile robotics, audio signal processing, and human-robot interaction, with a particular focus on enabling robots to perceive and respond to sound in real-world environments. His most influential work introduces the concept of Pitch-Cluster-Maps (PCMs), a computationally efficient sound database built on vector quantization and binarized frequency spectra. This method allows mobile robots to recognize daily sounds—such as a phone ringing or a door closing—in noisy home and office settings, a critical step toward practical domestic assistance. His most cited paper (17 citations) establishes PCMs as a lightweight, robust solution for sound identification, while subsequent work refines short-term recognition. Enomoto also explored ubiquitous sensing, combining ceiling-mounted ultrasonic locators and microphone arrays with onboard robot audio to detect user calls and navigate accordingly. In a notable study on loudness measurement, he analyzed how humans modulate their speech volume when addressing a robot in noisy conditions, informing the design of more natural interaction systems. Though his citation counts are modest, Enomoto’s contributions to robot audition and ubiquitous audio sensing have laid groundwork for context-aware, acoustically intelligent service robots.