Real-Time Implementation of a Combined PCA-ICA Algorithm for Blind Source Separation

In this thesis we introduce and investigate a method combining Principle Component Analysis (PCA) and Independent Component Analysis (ICA) for Blind Source Separation (BSS). A recursive method for the PCA is applied to meet the demands of a real-time application, and for the ICA algorithm, the Information maximization principle is used. In an effort to address convolutive BSS, the separation is performed in the frequency domain. By doing so, the problem reduces to the simple instantaneous case, and existing instantaneous BSS model can be used. However, frequency domain BSS is subject to both permutation and scaling ambiguities. This thesis examines several methods to solve these problems, like Direction Of Arrival (DOA) and the Kurtosis. Furthermore, results are presented based on Matlab simulations as well as from a real-time implementation. Evaluations show that the combined PCA-ICA algorithm outperforms both the PCA and ICA alone. The algorithm was also successfully implemented in real-time with comparable noise suppression capability compared to Matlab implementation. Future work which include ways to improve efficiency of the algorithms is also discussed.