Linked Life Data

20100076

Source:http://linkedlifedata.com/resource/pubmed/id/20100076

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
6
pubmed:dateCreated
2010-5-4
pubmed:abstractText
Convolutive mixtures of signals, which are common in acoustic environments, can be difficult to separate into their component sources. Here we present a uniform probabilistic framework to separate convolutive mixtures of acoustic signals using independent vector analysis (IVA), which is based on a joint distribution for the frequency components originating from the same source and is capable of preventing permutation disorder. Different gaussian mixture models (GMM) served as source priors, in contrast to the original IVA model, where all sources were modeled by identical multivariate Laplacian distributions. This flexible source prior enabled the IVA model to separate different type of signals. Three classes of models were derived and tested: noiseless IVA, online IVA, and noisy IVA. In the IVA model without sensor noise, the unmixing matrices were efficiently estimated by the expectation maximization (EM) algorithm. An online EM algorithm was derived for the online IVA algorithm to track the movement of the sources and separate them under nonstationary conditions. The noisy IVA model included the sensor noise and combined denoising with separation. An EM algorithm was developed that found the model parameters and separated the sources simultaneously. These algorithms were applied to separate mixtures of speech and music. Performance as measured by the signal-to-interference ratio (SIR) was substantial for all three models.
pubmed:grant
pubmed:commentsCorrections
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Jun
pubmed:issn
1530-888X
pubmed:author
pubmed:issnType
Electronic
pubmed:volume
22
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
1646-73
pubmed:dateRevised
2010-9-28
pubmed:meshHeading
pubmed:year
2010
pubmed:articleTitle
Independent vector analysis for source separation using a mixture of gaussians prior.
pubmed:affiliation
Computational Neurobiology Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, 92037, USA. jhao@ucsd.edu
pubmed:publicationType
Journal Article