Papers
6
Total Citations
78
H-Index
4
About
Laurent Girin is a leading researcher at the intersection of audio signal processing, machine learning, and robotics. His work focuses on enabling robots to perceive and interact with their environment through sound, with key contributions in sound source localization, separation, and classification. Girin is best known for pioneering audio-visual fusion techniques that leverage the complementary strengths of hearing and vision to robustly track multiple speakers in noisy, real-world settings—a critical capability for human-robot interaction. His landmark 2014 study on sound representation and classification for domestic robots, which introduced a challenging dataset recorded under realistic conditions (background noise, reverberations, and multiple sources), has garnered 29 citations and set a benchmark for the field. More recently, his work on variational fusion for multi-person tracking (2019) and autonomous sensorimotor learning for sound localization (2018) continues to push boundaries. With over 70 citations across his most influential papers, Girin’s research is essential reading for anyone working on robotic audition, multi-modal perception, or audio processing in unconstrained environments.
Research Focus
Key Achievements
Top Papers
- 1Sound representation and classification benchmark for domestic robots29 citations · 2014
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- 4Audio source separation into the wild7 citations · 2018
- 5Audio-Visual Variational Fusion for Multi-Person Tracking with Robots4 citations · 2019
- 6Sound Representation and Classification Benchmark for Domestic Robots4 citations · 2014