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About

Dick Botteldooren is a leading researcher at the intersection of acoustic scene analysis and human-robot interaction (HRI), where his work bridges machine listening with socially intelligent robotics. His key research areas include acoustic scene classification (ASC), audio event detection, and the modeling of human-robot tactile and speech interactions. A major contribution is his pioneering work on cooperative scene-event modeling for ASC, which leverages the natural human ability to relate acoustic scenes to specific audio events, achieving a nuanced understanding of context that surpasses traditional independent classification methods. This work has garnered significant attention, with his most cited paper accumulating 14 citations. Botteldooren has also made notable strides in behavioral modeling, demonstrating how the intensity of tactile interaction with a robot influences human risk-taking, and in speech adaptation, where he develops algorithms that allow robots to dynamically adjust their vocal output for enhanced intelligibility in varied environments. His recent 2025 study on sound-based recognition of touch gestures and emotions further advances HRI by enabling robots like Pepper and Nao to interpret tactile cues without full-body skin, opening new pathways for more natural, empathetic human-robot collaboration.