Marcel Mueglich
Papers
1
Total Citations
2
H-Index
1
About
Marcel Müglich is a researcher whose work sits at the intersection of acoustic signal processing and humanoid robotics. His primary research focus is on developing advanced beamforming techniques that account for the physical presence of a robot’s head, a critical challenge for enabling robots to localize and interpret sounds in real-world environments. His most notable contribution, the "HRTF-based robust least-squares frequency-invariant polynomial beamforming" method, directly addresses how a robot’s own body distorts incoming sound fields. By integrating Head-Related Transfer Functions (HRTFs) into a robust polynomial beamformer design, Müglich’s work allows for flexible, frequency-invariant steering of acoustic beams—a significant step toward making robot hearing more reliable and human-like. While his highly specialized work has garnered 2 citations on his seminal 2016 paper, its impact lies in laying the algorithmic groundwork for future systems in human-robot interaction, where spatial hearing is essential. Müglich’s research is particularly valuable for students and engineers working on auditory scene analysis for autonomous systems, as it bridges the gap between theoretical array processing and the messy, real-world acoustics of a moving robot.
Research Focus
Key Achievements
Top Papers
- 1