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Source separation

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Title: Source separation  
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Source separation

Source separation problems in digital signal processing are those in which several signals have been mixed together into a combined signal and the objective is to recover the original component signals from the combined signal. The classical example of a source separation problem is the cocktail party problem, where a number of people are talking simultaneously in a room (for example, at a cocktail party), and a listener is trying to follow one of the discussions. The human brain can handle this sort of auditory source separation problem, but it is a difficult problem in digital signal processing. This was first analyzed by Colin Cherry.

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.

The human brain must also solve this problem in real time. In human perception this ability is commonly referred to as auditory scene analysis or the cocktail party effect.

Applications

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 yield an accurate 3D-picture of the interior of the head. However, external sources of electromagnetic fields, such as a wristwatch on the subject's 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.

Another application is the separation of musical signals. For a stereo mix of relatively simple signals it is now possible to make a pretty accurate separation, although some artifacts remain.

See also

External links

  • 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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