Signal separation: Difference between revisions
Wrote something on the top of my head - please, someone, check the facts |
what does "cueued" mean? I didn't know how to fix it... :) |
||
| Line 1: | Line 1: | ||
Source separation refers to a class of problems within [[digital signal processing]], where 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 persons are talking simultaneously in a room, like on 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. |
Source separation refers to a class of problems within [[digital signal processing]], where 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 persons are talking simultaneously in a room, like on 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 succesfull approaches are [[Principal component analysis]] and [[Independent component 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 succesfull approaches are [[Principal component analysis]] and [[Independent component analysis]]. |
||
| ⚫ | One of the practical applications having cueued research in this area, is medical imaging of the brain with [[MRI]]. 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 artefacts from the signal. |
||
| ⚫ | One of the practical applications having **cueued** research in this area, is medical imaging of the brain with [[MRI]]. 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 artefacts from the signal. |
||
External resources: |
External resources: |
||
* http://www.cis.hut.fi/projects/ica/ |
* http://www.cis.hut.fi/projects/ica/ |
||
Revision as of 09:39, 6 November 2001
Source separation refers to a class of problems within digital signal processing, where 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 persons are talking simultaneously in a room, like on 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 succesfull approaches are Principal component analysis and Independent component analysis.
One of the practical applications having **cueued** research in this area, is medical imaging of the brain with MRI. 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 artefacts from the signal.
External resources: