--- abstract: |2 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. altloc: [] chapter: ~ commentary: ~ commref: ~ confdates: 22.9.-24.9.2004 conference: 5th International conference on independent component analysis and blind signal separation confloc: 'Granada, Spain' contact_email: ~ creators_id: - 4893 - 4715 creators_name: - family: Valpola given: Harri honourific: Dr lineage: '' - family: Särelä given: Jaakko honourific: Mr lineage: '' date: 2004 date_type: published datestamp: 2004-05-24 department: ~ dir: disk0/00/00/36/37 edit_lock_since: ~ edit_lock_until: ~ edit_lock_user: ~ editors_id: [] editors_name: [] eprint_status: archive eprintid: 3637 fileinfo: /style/images/fileicons/application_pdf.png;/3637/1/ICA04_rev.pdf full_text_status: public importid: ~ institution: ~ isbn: ~ ispublished: inpress issn: ~ item_issues_comment: [] item_issues_count: 0 item_issues_description: [] item_issues_id: [] item_issues_reported_by: [] item_issues_resolved_by: [] item_issues_status: [] item_issues_timestamp: [] item_issues_type: [] keywords: 'denoising source separation, DSS, independent component analysis, ICA, blind source separation, BSS, FastICA, stability' lastmod: 2011-03-11 08:55:36 latitude: ~ longitude: ~ metadata_visibility: show note: ~ number: ~ pagerange: ~ pubdom: FALSE publication: ~ publisher: ~ refereed: TRUE referencetext: ~ relation_type: [] relation_uri: [] reportno: ~ rev_number: 12 series: ~ source: ~ status_changed: 2007-09-12 16:52:30 subjects: - comp-sci-stat-model - comp-sci-mach-learn - comp-sci-neural-nets - comp-sci-art-intel succeeds: ~ suggestions: ~ sword_depositor: ~ sword_slug: ~ thesistype: ~ title: 'Accurate, fast and stable denoising source separation algorithms' type: confpaper userid: 4715 volume: ~