Abstract:
Blind Source Separation (BSS) is a statistical approach to separating individual signals from an observed mixture of a group of signals.
BSS relies on only very weak assumptions on the signals and the mixing process and this blindness enables the technique to be used in a wide variety of situations.
Research in the field of Blind Source Separation has resulted in the development of a family of algorithms, known as Independent Component
Analysis (ICA) algorithms, that can reliably and efficiently achieve blind separation of signals.
There are two important problems that are generally considered: instantaneous BSS and convolutive BSS.
The difference between these two is based on the nature of the signal mixing process.
In this thesis, the mathematical foundations of both instanta-neous and convolutive BSS are developed.
Once this mathematical framework has been established, the emphasis of the thesis moves to experimental results obtained with ICA techniques .
