Geometric Source Separation: Merging convolutive source separation with geometric beamforming – Lucas Parra (Sarnoff Corporation)
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Blind source separation of broad band signals in a multi-path environment remains a difficult problem. Robustness has been limited due to frequency permutation ambiguities. In principle, increasing the number of sensors allows improved performance but also introduces additional degrees of freedom in the separating filters that are not fully determined by separation criteria. We propose here to further shape the filters and improve the robustness of blind separation by including geometric information such as sensor positions and localized source assumption. This allows us to combine blind source separation with notions from adaptive and geometric beamforming leading to a number of novel algorithms that could be termed collectively “geometric source separation”.
Lucas C. Parra was born in Tucuman, Argentina. He received his Diploma in Physics in 1992, and Doctorate in Physics in 1996 from the Maximilian-Ludwig-University, Munich, Germany. From 1992 to 1995 he worked at the Neural Networks Group at Siemens Central Research in Munich, Germany and at the Machine Learning Department at Siemens Corporate Research (SCR) in Princeton, NJ. During 1995-1997 he was member of the Imaging Department at SCR and worked on medical image processing and novel reconstruction algorithms for nuclear medicine. Since 1997 he is with Sarnoff Corp. His current research concentrates on probabilistic models in various image and signal processing areas.