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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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Published in: | Signal processing 2008-06, Vol.88 (6), p.1528-1538 |
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container_title | Signal processing |
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creator | Chatterjee, Saikat Sreenivas, T.V. |
description | 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. |
doi_str_mv | 10.1016/j.sigpro.2008.01.001 |
format | article |
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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 |
title | Optimum switched split vector quantization of LSF parameters |
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