Signal and system modelling: algorithms, implementations and applications

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While we may not recognize it, as people our senses never receive input in a clean, organized form.  Typically, audible signals are a mixture of conversation of several people in the same room, desirable speech mixed with background traffic noise, etc.  While we do hear the full mixture, we routinely manage to separate these signals and focus on the one that we deem important.  We are also able to shift attention quickly when a new signal arrives depending on the importance we attach to such a signal (e.g. fire alarm).  Man-made systems attempt to simulate this powerful performance of human beings in various applications.  For example, measuring the heart beat of a baby in its mother's womb will necessitate separating the fetus's heart beat from the mother's own heart beat since any sensing device will only be able to measure a mixture of both.  Similarly, microphones in a multiple speaker room will invariably pick up more than the desired speaker.  Hearing aids for a hearing impaired person in a party environment or even a kindergarten class will pick up the speech of all individuals around and some mechanism will have to determine which signals are undesirable, remove them and focus on the desired one. This research will study the signal processing aspects associated with the above problem with the objective of developing faster, lower complexity algorithms that can effectively model signals and systems with the goal of applying these algorithms to improve performance of hearing aids as one possible application.