Julie Wall

Papers

Total Citations

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About

Julie Wall’s research lies at the intersection of mobile robotics, auditory perception, and neuromorphic computing. Her most cited work, “A comparison of sound localisation techniques using cross-correlation and spiking neural networks for mobile robotics” (2011, 4 citations), introduces a novel approach to enabling robots to locate sound sources in noisy, dynamic environments. In this study, she developed both a cross-correlation algorithm and a spiking neural network (SNN) architecture that mimics the mammalian auditory system’s sound localisation ability. By testing these techniques with real-world recordings on a mobile robot, Wall demonstrated how biologically inspired models can outperform traditional methods in challenging acoustic conditions. Her contributions advance the field of autonomous systems, particularly in human-robot interaction and assistive robotics, where accurate auditory perception is critical. Though her citation count is modest, her work is foundational for researchers exploring neuromorphic solutions to sensory processing. Wall’s interdisciplinary approach—bridging neuroscience, signal processing, and robotics—highlights her commitment to creating more adaptive, intelligent machines that can navigate and interact with the world as humans do.