Blind Source Separation Exploiting Higher-Order Frequency Dependencies

By Taesu Kim, Hagai Thomas Attias, Soo-Young Lee, Te-Won Lee. IEEE Transactions on Audio, Speech, and Language Processing, 2007

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AMICA : An Adaptive Mixture of Independent Component Analyzers with Shared Components (2011, methodology): “We have also extended the model to include mixtures of linear processes [16], where blind deconvolution is treated in a manner similar to [17]–[20], as well as complex ICA [21] and dependent sources [21]–[23].”
Joint Blind Source Separation by Multi-set Canonical Correlation Analysis (2009, background): “It is observed that the pair of sources assume correlation whereas in IVA, the corresponding sources within an independent source vector are assumed to have a multivariate distribution with no second-order dependence [3].”
Determined Blind Source Separation Unifying Independent Vector Analysis and Nonnegative Matrix Factorization (2016, methodology): “IVA [16]–[18] is a multivariate extension of FDICA and can solve the permutation problem [12]–[14].”
Understanding and Modeling of WiFi Signal Based Human Activity Recognition (2015, methodology): “Therefore, we can use blind signal separation methods [15] to extract CFR power distortions caused by different person.”
Stable and fast update rules for independent vector analysis based on auxiliary function technique (2011, background): “Index Terms— independent vector analysis, blind source separation, natural gradient, auxiliary function”