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About

Walter Kellermann is a prominent researcher in acoustic signal processing, with expertise spanning microphone array processing, acoustic source localization, robot audition, and autonomous systems perception. His work addresses some of the most demanding challenges in real-world audio environments, where noise, reverberation, and interference severely degrade acoustic sensing capabilities. Kellermann's most impactful contribution is his leadership in establishing the LOCATA Challenge, which provided a standardized corpus for benchmarking acoustic source localization and tracking algorithms — a landmark resource that has garnered over 100 citations and helped unify evaluation practices across the research community. His research into robot audition has been particularly pioneering, tackling the notoriously difficult problem of ego-noise suppression — filtering out the sounds robots generate through their own movement — using innovative multichannel dictionary approaches guided by motor data. Beyond robotics, Kellermann has advanced the concept of "acoustic self-awareness" in autonomous systems, arguing that sound perception is a critical and underexplored modality for intelligent machines navigating complex environments. His contributions to sparse signal representation, HRTF-based beamforming, and adaptive microphone array topologies further demonstrate a researcher whose influence spans both foundational signal processing theory and its practical deployment in intelligent, human-interacting systems.