Optimum switched split vector quantization of LSF parameters

We address the issue of rate–distortion (R/D) performance optimality of the recently proposed switched split vector quantization (SSVQ) method. The distribution of the source is modeled using Gaussian mixture density and thus, the non-parametric SSVQ is analyzed in a parametric model based framework...

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Bibliographic Details
Published in:Signal processing 2008-06, Vol.88 (6), p.1528-1538
Main Authors: Chatterjee, Saikat, Sreenivas, T.V.
Format: Article
Language:English
Subjects:
Applied sciences
Coding, codes
Exact sciences and technology
Gaussian mixture model
Information, signal and communications theory
LPC quantization
Sampling, quantization
Signal and communications theory
Signal processing
speech coding
Speech processing
SRA - ICT
SRA - Informations- och kommunikationsteknik
Telecommunications and information theory
Vector quantization
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Summary:We address the issue of rate–distortion (R/D) performance optimality of the recently proposed switched split vector quantization (SSVQ) method. The distribution of the source is modeled using Gaussian mixture density and thus, the non-parametric SSVQ is analyzed in a parametric model based framework for achieving optimum R/D performance. Using high rate quantization theory, we derive the optimum bit allocation formulae for the intra-cluster split vector quantizer (SVQ) and the inter-cluster switching. For the wide-band speech line spectrum frequency (LSF) parameter quantization, it is shown that the Gaussian mixture model (GMM) based optimum parametric SSVQ method provides 1 bit/vector advantage over the non-parametric SSVQ method.
ISSN:0165-1684
1872-7557
1872-7557
DOI:10.1016/j.sigpro.2008.01.001