Min-Jian Liao

Papers

Total Citations

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About

Min-Jian Liao is a researcher whose work bridges signal processing and interactive robotics, with a primary focus on improving direction-of-arrival (DOA) estimation in challenging acoustic environments. His key contributions center on developing robust algorithms that enable robots to localize sound sources accurately despite high levels of background noise—a critical capability for human-robot interaction. Liao's most cited work, "Subspace-based DOA with linear phase approximation and frequency bin selection preprocessing for interactive robots in noisy environments" (2015), introduces a novel preprocessing technique that selectively filters frequency bins and applies linear phase approximations to enhance subspace-based DOA methods. This approach significantly improves localization accuracy in real-world noisy settings, directly addressing a fundamental challenge in auditory robotics. While his citation count of 3 reflects the specialized nature of this early work, Liao's contributions are notable for their practical orientation toward interactive systems, where reliable sound source localization is essential for natural communication. His research demonstrates a thoughtful integration of theoretical signal processing with applied robotics, offering valuable insights for engineers developing more perceptive and responsive autonomous systems.