Papers
4
Total Citations
68
H-Index
3
About
Zihan Pan is a leading researcher in biologically inspired auditory systems, with a primary focus on sound source localization (SSL) for robotics and human-robot interaction. His work bridges computational neuroscience and engineering, most notably through the development of the "Multi-Tone Phase Coding" (MTPC) model, which emulates the mammalian auditory pathway using spiking neural networks (SNNs) to achieve precise SSL in noisy environments. This foundational paper has garnered 33 citations and is complemented by his work on the GCC-PHAT algorithm enhanced with speech-oriented attention, which addresses SSL degradation in reverberant conditions (20 citations). Pan’s research extends to the "HuRAI" model, a brain-inspired computational framework for human-robot auditory interfaces, demonstrating his commitment to creating robust, real-time robotic audition systems. His contributions are distinguished by their practical implementation—successfully deploying SNN-based algorithms on robotic platforms with microphone arrays—and their potential to revolutionize how robots perceive and interact with complex acoustic environments. With a growing citation impact, Pan is shaping the future of intelligent auditory processing.
Research Focus
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