creators_name: Valpola, Harri creators_name: Särelä, Jaakko creators_id: 4893 creators_id: 4715 type: confpaper datestamp: 2004-05-24 lastmod: 2011-03-11 08:55:36 metadata_visibility: show title: Accurate, fast and stable denoising source separation algorithms ispublished: inpress subjects: comp-sci-stat-model subjects: comp-sci-mach-learn subjects: comp-sci-neural-nets subjects: comp-sci-art-intel full_text_status: public keywords: denoising source separation, DSS, independent component analysis, ICA, blind source separation, BSS, FastICA, stability abstract: 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. date: 2004 date_type: published refereed: TRUE citation: Valpola, Dr Harri and Särelä, Mr Jaakko (2004) Accurate, fast and stable denoising source separation algorithms. [Conference Paper] (In Press) document_url: http://cogprints.org/3637/1/ICA04_rev.pdf