About: Signal separation

Facets (new session)
Description
Metadata
Settings
- Rule:
- Inverse Functional Properties:
- "Same As":

About: Signal separation Goto Sponge NotDistinct Permalink

An Entity of Type : owl:Thing, within Data Space : dbpedia.demo.openlinksw.com associated with source document(s)
QRcode icon

http://dbpedia.demo.openlinksw.com/describe/?url=http%3A%2F%2Fdbpedia.org%2Fresource%2FSignal_separation

Source separation, blind signal separation (BSS) or blind source separation, is the separation of a set of source signals from a set of mixed signals, without the aid of information (or with very little information) about the source signals or the mixing process. It is most commonly applied in digital signal processing and involves the analysis of mixtures of signals; the objective is to recover the original component signals from a mixture signal. The classical example of a source separation problem is the cocktail party problem, where a number of people are talking simultaneously in a room (for example, at a cocktail party), and a listener is trying to follow one of the discussions. The human brain can handle this sort of auditory source separation problem, but it is a difficult problem

Attributes	Values
rdf:type	Thing
rdfs:label	Signal separation (en)
rdfs:comment	Source separation, blind signal separation (BSS) or blind source separation, is the separation of a set of source signals from a set of mixed signals, without the aid of information (or with very little information) about the source signals or the mixing process. It is most commonly applied in digital signal processing and involves the analysis of mixtures of signals; the objective is to recover the original component signals from a mixture signal. The classical example of a source separation problem is the cocktail party problem, where a number of people are talking simultaneously in a room (for example, at a cocktail party), and a listener is trying to follow one of the discussions. The human brain can handle this sort of auditory source separation problem, but it is a difficult problem (en)
rdfs:seeAlso	Cocktail party effect
foaf:depiction
dcterms:subject	Speech processing Digital signal processing
Wikipage page ID	28804 (xsd:integer)
Wikipage revision ID	1121170449 (xsd:integer)
Link from a Wikipage to another Wikipage	Electromagnetic field Non-negative matrix factorization Nonnegative matrix factorization Basis (linear algebra) Speech processing Deconvolution Dependent component analysis Independent component analysis Correlation Medical imaging Colin Cherry Electroencephalogram Music Underdetermined system Magnetic field Signal processing Singular value decomposition Common spatial pattern Computational auditory scene analysis Celemony Software Joint Approximation Diagonalization of Eigen-matrices Stationary subspace analysis Digital signal processing Digital image Cocktail party problem Magnetoencephalography Tensors Cocktail party Cocktail party effect Digital signal processing Auditory scene analysis Information theory Speech segmentation Factorial code Sonic artifact Principal components analysis Independence (probability) Independent components analysis Signal (information theory) Adaptive filtering Infomax principle Segmentation (image processing) Sparsity Multidimensional data dbr:Low-complexity_coding_and_decoding
Link from a Wikipage to an external page	http://shogun-toolbox.org/ http://www.volker-koch.com/diss/ https://www.python.org/ http://www.cis.hut.fi/projects/ica/ http://apps.dtic.mil/dtic/tr/fulltext/u2/a455940.pdf https://web.archive.org/web/20061128090454/http:/visl.technion.ac.il/demos/bss/
sameAs	Signal separation Signal separation Signal separation
dbp:wikiPageUsesTemplate	dbt:Authority_control dbt:Commons_category dbt:Other_uses dbt:Reflist dbt:See_also dbt:Short_description dbt:TOC_right
thumbnail	wiki-commons:Special:FilePath/Polyphonic_note_separation_&_manipulation.jpg?width=300
Link from a Wikipa... related subject.	dbpedia-de:Cocktail-Party-Effekt
has abstract	Source separation, blind signal separation (BSS) or blind source separation, is the separation of a set of source signals from a set of mixed signals, without the aid of information (or with very little information) about the source signals or the mixing process. It is most commonly applied in digital signal processing and involves the analysis of mixtures of signals; the objective is to recover the original component signals from a mixture signal. The classical example of a source separation problem is the cocktail party problem, where a number of people are talking simultaneously in a room (for example, at a cocktail party), and a listener is trying to follow one of the discussions. The human brain can handle this sort of auditory source separation problem, but it is a difficult problem in digital signal processing. This problem is in general highly underdetermined, but useful solutions can be derived under a surprising variety of conditions. Much of the early literature in this field focuses on the separation of temporal signals such as audio. However, blind signal separation is now routinely performed on multidimensional data, such as images and tensors, which may involve no time dimension whatsoever. Several approaches have been proposed for the solution of this problem but development is currently still very much in progress. Some of the more successful approaches are principal components analysis and independent component analysis, which work well when there are no delays or echoes present; that is, the problem is simplified a great deal. The field of computational auditory scene analysis attempts to achieve auditory source separation using an approach that is based on human hearing. The human brain must also solve this problem in real time. In human perception this ability is commonly referred to as auditory scene analysis or the cocktail party effect. (en)
prov:wasDerivedFrom	wikipedia-en:Signal_separation?oldid=1121170449&ns=0
page length (characters) of wiki page	10178 (xsd:nonNegativeInteger)
foaf:isPrimaryTopicOf	wikipedia-en:Signal_separation
is Link from a Wikipage to another Wikipage of	Empirical orthogonal functions Blind source separation Source separation Daniel Levitin Real-time analyzer Blind signal separation Auditory scene analysis Shoko Araki Self-modeling mixture analysis Multivariate Curve Resolution Multivariate curve resolution
is Wikipage redirect of	Blind source separation Blind signal separation Self-modeling mixture analysis Multivariate Curve Resolution Multivariate curve resolution
is foaf:primaryTopic of	wikipedia-en:Signal_separation

Faceted Search & Find service v1.17_git139 as of Feb 29 2024

Alternative Linked Data Documents: ODE Content Formats:

RDF

ODATA

Microdata

About

OpenLink Virtuoso version 08.03.3330 as of Mar 19 2024, on Linux (x86_64-generic-linux-glibc212), Single-Server Edition (378 GB total memory, 60 GB memory in use)
Data on this page belongs to its respective rights holders.
Virtuoso Faceted Browser Copyright © 2009-2024 OpenLink Software