Marko Ilievski

University of Lethbridge, University of Waterloo

Papers

Total Citations

H-Index

About

Marko Ilievski is a researcher whose work spans computational auditory perception, binaural sound processing, and autonomous systems. His research has made notable contributions to the challenging problem of auditory scene analysis — the computational separation and localization of sounds in complex acoustic environments — with a particular focus on binaural hearing systems designed for humanoid robots. His most cited work, "A Bayesian Computational Basis for Auditory Selective Attention Using Head Rotation and the Interaural Time-Difference Cue" (2017, 14 citations), demonstrates how Bayesian probabilistic frameworks can be leveraged to mimic the way animals use binaural cues, such as interaural time differences, to isolate individual sound streams from acoustic mixtures. Building on this foundation, his 2019 follow-up paper introduced a noise-robust binaural sound localization system for humanoid robots, advancing the practical deployment of such methods in real-world environments (7 citations). Ilievski has also contributed to autonomous vehicle research, developing WiseBench, a motion planning benchmarking framework that addresses a gap in standardized evaluation tools for autonomous driving systems (2020, 3 citations). Together, his body of work reflects a commitment to bridging theoretical probabilistic modeling with practical robotics and autonomous systems applications.