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Source separation problems in digital signal processing are those in which several signals have been mixed together and the objective is to find out what the original signals were. The classical example is the "cocktail party problem", where a number of people are talking simultaneously in a room (like at a cocktail party), and one is trying to follow one of the discussions. The human brain can handle this sort of auditory source separation problem, but it is a very tricky problem in digital signal processing.
Several approaches have been proposed for the solution of this problem but development is currently still very much in progress. Some of the more successful approaches are principal components analysis and independent components analysis, which work well when there are no delays or echoes present; that is, the problem is simplified a great deal. The field of computational auditory scene analysis attempts to achieve auditory source separation using an approach that is based on human hearing.
One of the practical applications being researched in this area is medical imaging of the brain with magnetoencephalography (MEG). This kind of imaging involves careful measurements of magnetic fields outside the head which yields an accurate 3D-picture of the interior of the head. However, external sources of electromagnetic fields such as a wristwatch on the subjects arm, will significantly degrade the accuracy of the measurement. Applying source separation techniques on the measured signals can help remove undesired artifacts from the signal.
- Explanation of Independent Component Analysis (ICA)
- A tutorial-style dissertation by Volker Koch that introduces message-passing on factor graphs to decompose EMG signals
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