Prediction Transform GMM Vector Quantization for Wideband LSFs

Authors

  • Miguel Arjona Ramírez University of São Paulo

DOI:

https://doi.org/10.14209/jcis.2014.5

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.

Downloads

Download data is not yet available.

Author Biography

Miguel Arjona Ramírez, University of São Paulo

Associate Professor

Department of Electronic Systems Engineering, Escola Politécnica

 

Downloads

Published

2014-11-27

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

Issue

Vol. 29 No. 1 (2014)

Section

Regular Papers

License

Authors who publish in this journal agree to the following terms:

  1. Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a CC BY-NC 4.0 (Attribution-NonCommercial 4.0 International) that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
  2. Authors can enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
  3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) before and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).

___________

Received 2012-07-21
Accepted 2014-11-25
Published 2014-11-27