Prediction Transform GMM Vector Quantization for Wideband LSFs

  • Miguel Arjona Ramírez University of São Paulo
Keywords: prediction transform, vector quantization, line spectral frequencies, speech analysis, speech coding.

Abstract

Classified vector quantization (VQ) using a Gaussian Mixture Model (GMM) preclassifier is a powerful VQ method. A prediction-based lower-triangular transform (PLT) is proposed for the enhancement of VQ in each cluster. The PLT is defined for generic vector spaces in the context of the covariance method of linear prediction (LP). Optimal quantizer banks are designed in minimum noise structures whose codebooks are used for the proposed Cartesian split VQ (CSVQ), which improves their coding gain. CSVQ is tested forline spectral frequency (LSF) quantization of wideband speech spectra, revealing a comparable average performance to the Karhunen-Loève transform (KLT) at lower rates with reduced outlier generation and computational complexity.

Author Biography

Miguel Arjona Ramírez, University of São Paulo

Associate Professor

Department of Electronic Systems Engineering, Escola Politécnica

 

Published
27-11-2014
How to Cite
Arjona Ramírez, M. (2014). Prediction Transform GMM Vector Quantization for Wideband LSFs. Journal of Communication and Information Systems, 29(1). https://doi.org/10.14209/jcis.2014.5
Section
Regular Papers