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Toward optimal mixture model based vector quantization
KTH, School of Electrical Engineering (EES), Sound and Image Processing (Closed 130101).
2005 (English)In: 2005 Fifth International Conference on Information, Communications and Signal Processing, 2005, 1329-1333 p.Conference paper, Published paper (Refereed)
Abstract [en]

Gaussian mixture model (GMM) based vector quantization (VQ) using a data-dependent weighted Euclidean distortion measure is presented. It is shown how GMM-VQ can be improved by using GMMs that model the optimal VQ point density rather than the source probability density as is done in previous work. GMM training procedures as well as procedures for encoding and decoding that takes a weighted distortion measure into account are presented. The usefulness of the proposed procedures is demonstrated on a source derived from speech spectrum parameters.

Place, publisher, year, edition, pages
2005. 1329-1333 p.
Keyword [en]
Decoding, Parameter estimation, Probability, Signal distortion, Signal encoding, Vector quantization, Data-dependent weighted, Euclidean distortion, Probability density, Speech spectrum parameters, Gaussian noise (electronic)
National Category
Signal Processing
Identifiers
URN: urn:nbn:se:kth:diva-156273Scopus ID: 2-s2.0-34147115614ISBN: 0780392833 (print)ISBN: 9780780392830 (print)OAI: oai:DiVA.org:kth-156273DiVA: diva2:767537
Conference
2005 Fifth International Conference on Information, Communications and Signal Processing, 6 December 2005 through 9 December 2005, Bangkok, Thailand
Note

QC 20141201

Available from: 2014-12-01 Created: 2014-11-26 Last updated: 2016-12-21Bibliographically approved

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  • apa
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  • Other locale
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Output format
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  • asciidoc
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