Denoising Source Separation
Jaakko Särelä, Harri Valpola; 6(9):233−272, 2005.
Abstract
A new algorithmic framework called denoising source separation (DSS) is introduced. The main benefit of this framework is that it allows for the easy development of new source separation algorithms which can be optimised for specific problems. In this framework, source separation algorithms are constructed around denoising procedures. The resulting algorithms can range from almost blind to highly specialised source separation algorithms. Both simple linear and more complex nonlinear or adaptive denoising schemes are considered. Some existing independent component analysis algorithms are reinterpreted within the DSS framework and new, robust blind source separation algorithms are suggested. The framework is derived as a one-unit equivalent to an EM algorithm for source separation. However, in the DSS framework it is easy to utilise various kinds of denoising procedures which need not be based on generative models. In the experimental section, various DSS schemes are applied extensively to artificial data, to real magnetoencephalograms and to simulated CDMA mobile network signals. Finally, various extensions to the proposed DSS algorithms are considered. These include nonlinear observation mappings, hierarchical models and over-complete, nonorthogonal feature spaces. With these extensions, DSS appears to have relevance to many existing models of neural information processing.
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