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Independent component analysis using Gaussian mixture models

Independent component analysis using Gaussian mixture models
Independent component analysis using Gaussian mixture models
This paper discusses a method for performing independent component analysis exploiting Gaussian mixture models (GMMs). Previously most techniques that combine these methods have used GMMs to model the source signals. This paper considers a parsimonious method for modelling the observed signals. The GMM is fitted to the observed data using a modified version of the expectation maximisation algorithm.
Becker, T.
d429edb7-411d-4cf9-aaf2-de2231d8a350
White, P.R.
2dd2477b-5aa9-42e2-9d19-0806d994eaba
Becker, T.
d429edb7-411d-4cf9-aaf2-de2231d8a350
White, P.R.
2dd2477b-5aa9-42e2-9d19-0806d994eaba

Becker, T. and White, P.R. (2006) Independent component analysis using Gaussian mixture models. Seventh International Conference on Mathematics in Signal Processing. 18 - 20 Dec 2006. 4 pp .

Record type: Conference or Workshop Item (Paper)

Abstract

This paper discusses a method for performing independent component analysis exploiting Gaussian mixture models (GMMs). Previously most techniques that combine these methods have used GMMs to model the source signals. This paper considers a parsimonious method for modelling the observed signals. The GMM is fitted to the observed data using a modified version of the expectation maximisation algorithm.

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More information

Published date: 2006
Venue - Dates: Seventh International Conference on Mathematics in Signal Processing, 2006-12-18 - 2006-12-20

Identifiers

Local EPrints ID: 43531
URI: https://eprints.soton.ac.uk/id/eprint/43531
PURE UUID: 172826fd-fc40-4e57-ae69-b9f3e4ba61d6
ORCID for P.R. White: ORCID iD orcid.org/0000-0002-4787-8713

Catalogue record

Date deposited: 09 Feb 2007
Last modified: 14 Mar 2019 01:54

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Contributors

Author: T. Becker
Author: P.R. White ORCID iD

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