Max Robotics — Global Service Robot Directory + US Certification

About

Sharon Gannot is a leading researcher in acoustic signal processing, with a focus on robot audition, speech enhancement, and source localization. Her work addresses critical challenges in enabling robots to interact naturally in complex auditory environments, particularly through moving microphone arrays and multi-channel processing. Gannot has made seminal contributions to source tracking and separation, developing expectation-maximization methods for estimating acoustic echoes and time-of-arrivals (TOAs), which are essential for room geometry estimation and reflector localization. Her papers, such as "Source tracking using moving microphone arrays for robot audition" (26 citations) and "Estimation of acoustic echoes using expectation-maximization methods" (14 citations), demonstrate her impact on autonomous systems. She has also advanced data-driven approaches for multi-microphone speaker localization on manifolds and explored single-microphone separation in noisy, reverberant settings. Notably, her work extends to social robotics, including multilingual intent recognition and gerontological healthcare applications, highlighting the real-world relevance of her research. With a portfolio spanning theoretical bounds like the hybrid Cramér-Rao lower bound to practical datasets, Gannot's contributions are pivotal for next-generation human-robot interaction and assistive technologies.