Max Robotics — Global Service Robot Directory + US Certification

About

Weipeng He is a leading researcher at the intersection of machine listening, audio signal processing, and socially intelligent robotics. His primary contributions lie in developing deep neural network approaches for robust sound source localization and multi-speaker detection, particularly for challenging human-robot interaction (HRI) environments. His seminal 2018 paper, "Deep Neural Networks for Multiple Speaker Detection and Localization," with 200 citations, pioneered the use of neural networks to simultaneously detect and localize multiple speakers, moving beyond conventional, assumption-heavy signal processing techniques. He has also advanced the field by tackling the critical data scarcity problem in learning-based direction-of-arrival estimation through novel network adaptation and data augmentation strategies. Beyond algorithms, Dr. He has made significant practical contributions to the field. He was a key contributor to the EU-funded MuMMER project, developing a socially intelligent robot for public spaces, and co-created the accompanying MuMMER dataset—a valuable multimodal resource for multi-party HRI research. He also helped organize the IEEE SLT 2021 Alpha-Mini Speech Challenge, fostering progress in keyword spotting and sound source localization on humanoid robots. His work seamlessly bridges theoretical advances in audio perception with real-world robotic applications.