ePrints@IIScePrints@IISc Home | About | Browse | Latest Additions | Advanced Search | Contact | Help

Gaussian mixture model based switched split vector quantization of LSF parameters

Chatterjee, Saikat and Sreenivas, TV (2007) Gaussian mixture model based switched split vector quantization of LSF parameters. In: 7th IEEE International Symposium on Signal Processing and Information Technology, DEC 15-18, 2007, Cairo.

[img] PDF
gsf.pdf - Published Version
Restricted to Registered users only

Download (887Kb) | Request a copy
Official URL: http://ieeexplore.ieee.org/search/srchabstract.jsp...

Abstract

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 parametric SSVQ method provides 1 bit/vector advantage over the non-parametric SSVQ method.

Item Type: Conference Paper
Additional Information: Copyright 2007 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
Department/Centre: Division of Electrical Sciences > Electrical Communication Engineering
Date Deposited: 26 Mar 2010 09:35
Last Modified: 19 Sep 2010 05:57
URI: http://eprints.iisc.ernet.in/id/eprint/26318

Actions (login required)

View Item View Item