Kernel Additive Models for Source Separation
نویسندگان
چکیده
منابع مشابه
Shift-Invariant Kernel Additive Modelling for Audio Source Separation
A major goal in blind source separation to identify and separate sources is to model their inherent characteristics. While most state-ofthe-art approaches are supervised methods trained on large datasets, interest in non-data-driven approaches such as Kernel Additive Modelling (KAM) remains high due to their interpretability and adaptability. KAM performs the separation of a given source applyi...
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We address the problem of Blind Source Separation (BSS) of superimposed signals in situations where one signal has constant or slowly varying intensities at some consecutive locations and at the corresponding locations the other signal has highly varying intensities. Independent Component Analysis (ICA) is a major technique for Blind Source Separation and the existing ICA algorithms fail to est...
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By combining the batch algorithm with the kernel trick, an improved kernel blind source separation (IKBSS) is presented. The IKBSS has not only a better performance but also a less computational complexity compared to the original kernel blind source separation (KBSS).
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Neural Independent Component Analysis (ICA) algorithms based on unimodal source distributions provide acceptable performances in the case of Blind Source Separation (BSS) of super-gaussian sources. However, their convergence profiles are significantly slower in the case of sub-gaussian sources. In some situations it is necessary to deal with sub-gaussian signals in the form of noise or others. ...
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We propose a method for solving linear space-time variant blind source separation (BSS) problem with additive noise, x=As+n, on the “pixelby-pixel” basis i.e. assuming that unknown mixing matrix is different for every space or time location. Solution corresponds with the isothermal-To equilibrium of the free energy H =U-ToS contrast function where U represents the input/output energy exchange a...
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ژورنال
عنوان ژورنال: IEEE Transactions on Signal Processing
سال: 2014
ISSN: 1053-587X,1941-0476
DOI: 10.1109/tsp.2014.2332434