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Algorithms need to solve a source separation problem, also known as spike sorting.
User separation is achieved by solving a blind source separation problem.
In this context, we mention the blind sources separation (BSS) which is the most general form of source separation problem.
In blind source separation problem, identifiability relies on the independence of the sources.
For example, I am interested in the source separation problem and have worked on the theory of blind source separation and its application to the cocktail party problem and to seismic signal decomposition, as well as on image processing applications.
Finally, although the technique was developed for the astrophysical source separation problem in mind, it is general and it is applicable to other source separation problems as well.
Commonly, a blind source separation problem is briefly defined by its forward mixing model.
In this paper, similar to [7] and [31], we also consider multiuser stereo source separation problem.
The factorization problem in Expression 8 is close to a source separation problem, in which a number of sources are mixed into different channels.
Obtaining the factorization (varvec{B} varvec{A}), under this assumption, is equivalent to solving a blind source separation problem.
Here, by modeling mutational processes as a blind source separation problem, we introduce a computational framework that effectively addresses these questions.
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