Papers
8
Total Citations
104
H-Index
5
About
Jean-Luc Zarader is a researcher whose work sits at the intersection of robotics, machine learning, and auditory signal processing, with a particular focus on binaural sound localization and robotic audition. His most significant contributions center on developing intelligent systems that enable humanoid robots to perceive and interpret their acoustic environments with human-like precision. Zarader has pioneered learning-based approaches to sound source localization, tackling challenging real-world conditions such as reverberation and noise — problems that have long constrained artificial audition systems. His 2013 paper on robust binaural sound localization, his most cited work with 37 citations, exemplifies this direction, while his 2012 studies on binaural cues and multimodal audio-visual integration further cemented his standing in the field. Zarader has also made notable contributions to binaural speaker recognition for humanoid robots, an underexplored area he helped bring to greater scholarly attention. Beyond audition, his work extends into rehabilitation robotics, demonstrating a broader commitment to human-centered robotic systems. With a cumulative body of work totaling over 100 citations, Zarader's research has meaningfully advanced how robots listen, identify, and interact within complex acoustic environments.
Research Focus
Key Achievements
Top Papers
- 1A learning-based approach to robust binaural sound localization37 citations · 2013
- 2A binaural sound source localization method using auditive cues and vision28 citations · 2012
- 3Towards a systematic study of binaural cues14 citations · 2012
- 4From monaural to binaural speaker recognition for humanoid robots11 citations · 2010
- 5Binaural speaker recognition for humanoid robots6 citations · 2010
- 6Binaural Speaker Recognition for humanoid robots4 citations · 2010
- 7
- 8