2010 | OriginalPaper | Buchkapitel
Independent Component Analysis of Time/Position Varying Mixtures
verfasst von : Michael Shamis, Yehoshua Y. Zeevi
Erschienen in: Latent Variable Analysis and Signal Separation
Verlag: Springer Berlin Heidelberg
Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.
Wählen Sie Textabschnitte aus um mit Künstlicher Intelligenz passenden Patente zu finden. powered by
Markieren Sie Textabschnitte, um KI-gestützt weitere passende Inhalte zu finden. powered by
Blind Source Separation (BSS) is a well known problem that has been addressed in numerous studies in the last few decades. Most of the studies in this field address the problem of time/position invariant mixtures of multiple sources. Real problems are however usually not time and/or position invariant, and much more complicated. We present an extension of the Maximum Likelihood (ML) Independent Component Analysis (ICA) approach to time variant instantaneous mixtures.