About: Semi-blind Noise Extraction Using Partially Known Position of the Target Source

Facets (new session)
Description
Metadata
Settings
- owl:sameAs
- Inference Rule:

About: Semi-blind Noise Extraction Using Partially Known Position of the Target Source Goto Sponge NotDistinct Permalink

An Entity of Type : http://linked.opendata.cz/ontology/domain/vavai/Vysledek, within Data Space : linked.opendata.cz associated with source document(s)

Attributes	Values
rdf:type	skos:Concept http://linked.opendata.cz/ontology/domain/vavai/Vysledek
rdfs:seeAlso	http://apps.webofknowledge.com/full_record.do?product=WOS&search_mode=GeneralSearch&qid=6&SID=R2AScTwajtiw8IC62ji&page=1&doc=1
Description	An extracted noise signal provides important information for subsequent enhancement of a target signal. When the target's position is fixed, the noise extractor could be a target-cancellation filter derived in a noise-free situation. In this paper we consider a situation when such cancellation filters are prepared for a set of several possible positions of the target in advance. The set of filters is interpreted as prior information available for the noise extraction when the target's exact position is unknown. Our novel method looks for a linear combination of the prepared filters via Independent Component Analysis. The method yields a filter that has a better cancellation performance than the individual filters or filters based on a minimum variance principle. The method is tested in a highly noisy and reverberant real-world environment with moving target source and interferers. A post-processing by Wiener filter using the noise signal extracted by the method is able to improve signal-to-noise ratio of the target by up to 8 dB. An extracted noise signal provides important information for subsequent enhancement of a target signal. When the target's position is fixed, the noise extractor could be a target-cancellation filter derived in a noise-free situation. In this paper we consider a situation when such cancellation filters are prepared for a set of several possible positions of the target in advance. The set of filters is interpreted as prior information available for the noise extraction when the target's exact position is unknown. Our novel method looks for a linear combination of the prepared filters via Independent Component Analysis. The method yields a filter that has a better cancellation performance than the individual filters or filters based on a minimum variance principle. The method is tested in a highly noisy and reverberant real-world environment with moving target source and interferers. A post-processing by Wiener filter using the noise signal extracted by the method is able to improve signal-to-noise ratio of the target by up to 8 dB. (en)
Title	Semi-blind Noise Extraction Using Partially Known Position of the Target Source Semi-blind Noise Extraction Using Partially Known Position of the Target Source (en)
skos:prefLabel	Semi-blind Noise Extraction Using Partially Known Position of the Target Source Semi-blind Noise Extraction Using Partially Known Position of the Target Source (en)
skos:notation	RIV/46747885:24220/13:#0002869!RIV14-GA0-24220___
http://linked.open...avai/predkladatel	Fakulta mechatroniky, informatiky a mezioborových studií
http://linked.open...avai/riv/aktivita	I P
http://linked.open...avai/riv/aktivity	I, P(GAP103/11/1947)
http://linked.open...iv/cisloPeriodika	vol. 21, no. 10
http://linked.open...vai/riv/dodaniDat	2014
http://linked.open...aciTvurceVysledku	Koldovský, Zbyněk Málek, J.
http://linked.open.../riv/druhVysledku	J - Článek v odborném periodiku
http://linked.open...iv/duvernostUdaju	S - Úplné a pravdivé údaje nepodléhající ochraně podle zvláštních právních předpisů
http://linked.open...titaPredkladatele	Technická univerzita v Liberci / Fakulta mechatroniky, informatiky a mezioborových studií
http://linked.open...dnocenehoVysledku	104610
http://linked.open...ai/riv/idVysledku	RIV/46747885:24220/13:#0002869
http://linked.open...riv/jazykVysledku	eng - angličtina
http://linked.open.../riv/klicovaSlova	Independent component analysis (en)
http://linked.open.../riv/klicoveSlovo	Independent component analysis
http://linked.open...odStatuVydavatele	US - Spojené státy americké
http://linked.open...ontrolniKodProRIV	[B8F2C276EA30]
http://linked.open...i/riv/nazevZdroje	IEEE TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING
http://linked.open...in/vavai/riv/obor	JC
http://linked.open...ichTvurcuVysledku	2 (xsd:int)
http://linked.open...cetTvurcuVysledku	4 (xsd:int)
http://linked.open...vavai/riv/projekt	Methods of Latent Variable Analysis in Blind Speech and Audio Processing
http://linked.open...UplatneniVysledku	2013
http://linked.open...v/svazekPeriodika	Oct. 2013
http://linked.open...iv/tvurceVysledku	Koldovský, Zbyněk Málek, Jiří Tichavský, P. Nesta, F.
http://linked.open...ain/vavai/riv/wos	000322334900004
issn	1558-7916
number of pages	13 (xsd:int)
http://bibframe.org/vocab/doi	10.1109/TASL.2013.2264674
http://localhost/t...ganizacniJednotka	24220

Faceted Search & Find service v1.16.118 as of Jun 21 2024

Alternative Linked Data Documents: ODE Content Formats:

RDF

ODATA

Microdata

About

OpenLink Virtuoso version 07.20.3240 as of Jun 21 2024, on Linux (x86_64-pc-linux-gnu), Single-Server Edition (126 GB total memory, 12 GB memory in use)
Data on this page belongs to its respective rights holders.
Virtuoso Faceted Browser Copyright © 2009-2024 OpenLink Software