The observed conversations consist of mixtures of true conversations. In other words, mixed signal vectors are observed and the goal is to separate these observed signal vectors into true unobservable underlying source signal vectors. I have been developing Bayesian Blind Source Separation in order to apply it to functional magnetic resonance imaging. Functional magnetic resonance imaging is a relatively new technology that is a very active research area. Subjects are usually given experimental and control tasks in a periodic fashion while images are taken over time.
Typical analysis consists of the following.
The MRI cannot directly measure brain activity. What it does measure is the deoxygenated blood which is an indicator of brain activity. There are many involuntary cognitive activities continually occuring while the experiment is being performed. Examples of which are cardiac, respiration, digestive, and random thought activity. The task is to discriminate between the experimental activity and these involuntary activities.