Editing Signal separation (section)

==Applications==
[[Image:Polyphonic note separation & manipulation.jpg|thumb|polyphonic note separation]]

===Cocktail party problem===
{{See also|Cocktail party effect}}
At a cocktail party, there is a group of people talking at the same time. You have multiple microphones picking up mixed signals, but you want to isolate the speech of a single person. BSS can be used to separate the individual sources by using mixed signals. In the presence of noise, dedicated optimization criteria need to be used.

===Image processing===
[[File:BSS-example.png|thumb|Figure 2. Visual example of BSS]]
Figure 2 shows the basic concept of BSS.  The individual source signals are shown as well as the mixed signals which are received signals. BSS is used to separate the mixed signals with only knowing mixed signals and nothing about original signal or how they were mixed.  The separated signals are only approximations of the source signals.  The separated images, were separated using [[Python (programming language)|Python]] and the [[Shogun (toolbox)|Shogun toolbox]] using Joint Approximation Diagonalization of Eigen-matrices ([[Joint Approximation Diagonalization of Eigen-matrices|JADE]]) algorithm which is based on [[independent component analysis]], ICA.<ref>Kevin Hughes “Blind Source Separation on Images with Shogun” http://shogun-toolbox.org/static/notebook/current/bss_image.html</ref>   This toolbox method can be used with multi-dimensions but for an easy visual aspect images(2-D) were used.

===Medical imaging===
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 field]]s outside the head which yield an accurate 3D-picture of the interior of the head. However, external sources of [[electromagnetic field]]s, 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.

===EEG===
In [[electroencephalogram]] (EEG) and [[magnetoencephalography]] (MEG), the interference from muscle activity masks the desired signal from brain activity. BSS, however, can be used to separate the two so an accurate representation of brain activity may be achieved.<ref name=":0" /><ref>{{Cite journal|last1=Congedo|first1=Marco|last2=Gouy-Pailler|first2=Cedric|last3=Jutten|first3=Christian|date=December 2008|title=On the blind source separation of human electroencephalogram by approximate joint diagonalization of second order statistics.|url=https://hal.archives-ouvertes.fr/hal-00343628|journal=Clinical Neurophysiology|volume=119|issue=12|pages=2677–2686|doi=10.1016/j.clinph.2008.09.007|pmid=18993114|arxiv=0812.0494|s2cid=5835843 }}</ref>

===Music===
Another application is the separation of [[music]]al signals. For a stereo mix of relatively simple signals it is now possible to make a fairly accurate separation, although some [[Sonic artifact|artifacts]] remain.

===Others===
Other applications:<ref name=":0" />
* Communications
* Stock Prediction
* Seismic Monitoring
* Text Document Analysis