---
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: ~