Signal separation: Difference between revisions
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'''Source separation''' |
'''Source separation''' problems in [[digital signal processing]] are those in which several [[signal|signals]] have been mixed together and the objective is to find out what the original signals were. A 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. It is quite obvious that humans have the ability to solve the auditory source separation problem (i.e. the cocktail party problem) quite sufficiently, but unfortunately, it is a very tricky problem in digital signal processing. |
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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]]. |
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]]. |
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One of the practical applications being researched in this area is medical imaging of the brain with [[magnetoencephalography]]. 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 removing undesired artifacts from the signal. |
One of the practical applications being researched in this area is medical imaging of the brain with [[magnetoencephalography]]. 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 removing undesired artifacts from the signal. |
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Another application is the separation of [[music]]al signals. For a stereo mix of relatively simple signals it is now possible to make a pretty accurate separation, although some artefacts remain. |
Another application is the separation of [[music]]al signals. For a stereo mix of relatively simple signals it is now possible to make a pretty accurate separation, although some artefacts remain. |
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==External link== |
==External link== |
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Revision as of 20:29, 16 July 2004
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. A 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. It is quite obvious that humans have the ability to solve the auditory source separation problem (i.e. the cocktail party problem) quite sufficiently, but unfortunately, 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.
One of the practical applications being researched in this area is medical imaging of the brain with magnetoencephalography. 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 removing 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 artefacts remain.