Laurent Benaroya
Papers
1
Total Citations
19
H-Index
1
About
Laurent Benaroya is a leading researcher in audio signal processing, with a primary focus on sound source localization and separation. His work centers on developing advanced mathematical frameworks to understand complex acoustic environments, particularly through the use of non-negative tensor factorization (NTF). In his seminal 2018 paper, "Binaural Localization of Multiple Sound Sources by Non-Negative Tensor Factorization," Benaroya introduced a novel approach that leverages NTF to achieve sparse, multichannel representations of audio signals across time, frequency, and space. This method enables robust binaural localization of multiple sound sources even in realistic, unknown acoustic settings—a significant challenge in the field. With 19 citations, this work has become a key reference for researchers tackling spatial audio and computational auditory scene analysis. Benaroya’s contributions are instrumental in advancing technologies for hearing aids, robotics, and immersive audio systems, demonstrating how tensor-based methods can unlock new levels of precision in understanding and navigating complex soundscapes.
Research Focus
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Top Papers
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