Papers
5
Total Citations
42
H-Index
3
About
Mounira Maazaoui is a leading researcher in robot audition, specializing in blind source separation (BSS) and acoustic signal processing for humanoid robotics. Her work focuses on enabling robots to intelligently perceive and separate multiple sound sources in noisy, reverberant environments—a critical capability for social interaction. Maazaoui’s major contributions include developing adaptive BSS algorithms that integrate head-related transfer function (HRTF) beamforming preprocessing, significantly improving speech separation and noise reduction for microphone arrays embedded in robotic heads. Her two-stage approach, combining fixed beamforming with source separation, has been foundational for robust robot hearing, with key papers accumulating over 12 citations each. Notably, she contributed to the Romeo2 Project, a landmark initiative to create a humanoid assistant and companion for everyday life, where she advanced situation assessment for social intelligence. Maazaoui’s research bridges signal processing and artificial intelligence, offering practical solutions for robots to understand complex auditory scenes. Her work remains highly influential for students and engineers developing autonomous systems that require reliable auditory perception in real-world settings.
Research Focus
Key Achievements
Top Papers
- 1Adaptive blind source separation with HRTFs beamforming preprocessing12 citations · 2012
- 2Blind source separation for robot audition using fixed HRTF beamforming12 citations · 2012
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