Denoising source separation

Särelä, Mr Jaakko and Valpola, Dr Harri (2004) Denoising source separation. [Preprint]

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A new algorithmic framework called denoising source separation (DSS) is introduced. The main benefit of this framework is that it allows for easy development of new source separation algorithms which are optimised for specific problems. In this framework, source separation algorithms are constucted 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 DSS framework and new, robust blind source separation algorithms are suggested. Although DSS algorithms need not be explicitly based on objective functions, there is often an implicit objective function that is optimised. The exact relation between the denoising procedure and the objective function is derived and a useful approximation of the objective function is presented. 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 overcomplete, nonorthogonal feature spaces. With these extensions, DSS appears to have relevance to many existing models of neural information processing.

Item Type:Preprint
Keywords:blind source separation, BSS, prior information, denoising, denoising source separation, DSS, independent component analysis, ICA, magnetoencephalograms, MEG, CDMA
Subjects:Computer Science > Statistical Models
Computer Science > Machine Learning
Computer Science > Neural Nets
Computer Science > Artificial Intelligence
ID Code:3493
Deposited By: Särelä, Dr Jaakko
Deposited On:16 Mar 2004
Last Modified:11 Mar 2011 08:55

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