Prediction Transform GMM Vector Quantization for Wideband LSFs

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Miguel Arjona Ramírez


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.

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How to Cite
Arjona Ramírez, M. (2014). Prediction Transform GMM Vector Quantization for Wideband LSFs. Journal of Communication and Information Systems, 29(1).
Regular Papers
Author Biography

Miguel Arjona Ramírez, University of São Paulo

Associate Professor

Department of Electronic Systems Engineering, Escola Politécnica