Karim Abed‐Meraim

Télécom Paris, Laboratoire Traitement et Communication de l’Information, Laboratoire Traitement du Signal et de l'Image

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About

Karim Abed‐Meraim is a leading researcher in signal processing, with a core focus on blind source separation (BSS) and its application to robot audition. His work addresses the critical challenge of enabling robots to hear and understand speech in noisy, reverberant environments. Abed‐Meraim pioneered a two-stage approach that combines fixed beamforming using Head-Related Transfer Functions (HRTFs) with adaptive BSS algorithms. This method dramatically improves the separation of multiple sound sources by first reducing environmental noise and reverberation, making it highly effective for robotic platforms. His most cited papers, each garnering 12 citations, demonstrate the practical impact of this technique, particularly in microphone arrays embedded in robotic heads. By systematically studying the effect of varying sensor numbers and introducing a parameterized sparsity criterion for frequency-domain processing, he has advanced the robustness and efficiency of auditory scene analysis for humanoid robots. Abed‐Meraim’s contributions are foundational for creating robots that can interact naturally in complex auditory environments, bridging the gap between theoretical signal processing and real-world robotic perception.