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PDF-optimized LSF vector quantization based on beta mixture models
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- Ma, Zhanyu (author)
- KTH,Ljud- och bildbehandling
-
- Leijon, Arne (author)
- KTH,Ljud- och bildbehandling
- (creator_code:org_t)
- 2010
- 2010
- English.
- In: Proceedings of the 11th Annual Conference of the International Speech Communication Association, INTERSPEECH 2010. - 9781617821233 ; , s. 2374-2377
- Conference paper (peer-reviewed)
Abstract
Subject headings
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- The line spectral frequencies (LSF) are known to be the mostefficient representation of the linear predictive coding (LPC) parametersfrom both the distortion and perceptual point of view.By considering the bounded property of the LSF parameters,we apply beta mixture models (BMM) to model the distributionof the LSF parameters. Meanwhile, by following the principlesof probability density function (PDF) optimized vector quantization(VQ), we derive the bit allocation strategy for the BMM.The LSF parameters are obtained from the TIMIT database anda practical VQ is designed. By taking the Bayesian informationcriterion (BIC), the square error (SE) and the spectral distortion(SD) as the criteria, the BMM based VQ outperforms theGaussian mixture model based VQ with uncorrelated Gaussiancomponent (UGMVQ) by about 1-2 bits/vector.
Subject headings
- TEKNIK OCH TEKNOLOGIER -- Elektroteknik och elektronik -- Annan elektroteknik och elektronik (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Electrical Engineering, Electronic Engineering, Information Engineering -- Other Electrical Engineering, Electronic Engineering, Information Engineering (hsv//eng)
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datavetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Sciences (hsv//eng)
Keyword
- speech coding
- vector quantization
- line spectral frequencies
- beta mixture model
- Gaussian mixture model
- Electrical engineering
- Elektroteknik
- Computer science
- Datalogi
- SRA - ICT
- SRA - Informations- och kommunikationsteknik
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- ref (subject category)
- kon (subject category)
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