Accurate, fast and stable denoising source separation algorithms

Valpola, Dr Harri and Särelä, Mr Jaakko (2004) Accurate, fast and stable denoising source separation algorithms. [Conference Paper] (In Press)

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Denoising source separation is a recently introduced framework for building source separation algorithms around denoising procedures. Two developments are reported here. First, a new scheme for accelerating and stabilising convergence by controlling step sizes is introduced. Second, a novel signal-variance based denoising function is proposed. Estimates of variances of different source are whitened which actively promotes separation of sources. Experiments with artificial data and real magnetoencephalograms demonstrate that the developed algorithms are accurate, fast and stable.

Item Type:Conference Paper
Keywords:denoising source separation, DSS, independent component analysis, ICA, blind source separation, BSS, FastICA, stability
Subjects:Computer Science > Statistical Models
Computer Science > Machine Learning
Computer Science > Neural Nets
Computer Science > Artificial Intelligence
ID Code:3637
Deposited By: Särelä, Dr Jaakko
Deposited On:24 May 2004
Last Modified:11 Mar 2011 08:55


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