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International Conference on Speech and Computer

SPECOM 2015: Speech and Computer pp 472–479Cite as

Voicing-Based Classified Split Vector Quantizer for Efficient Coding of AMR-WB ISF Parameters

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9319))

Abstract

Modern speech coders necessitate efficient coding of the linear predictive coding (LPC) coefficients. Line spectral Frequencies (LSF) and Immittance Spectral Frequencies (ISF) parameters are currently the most efficient choices of transmission parameters for the LPC coefficients. In this paper, we present a voicing-based classified split vector quantization scheme developed for efficient coding of wideband AMR-WB G.722.2 ISF (Immittance Spectral Frequencies) parameters under noiseless channel conditions. It was designed based on the classified vector quantization (CVQ) structure combined with the split vector quantization (SVQ). Simulation results will show that the new ISF coding scheme, called ISF-CSVQ coder, performs better than the conventional non classified ISF-SVQ, while saving several bits per frame.

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Author information

Authors and Affiliations

  1. Speech Communication and Signal Processing Laboratory, USTHB University, P.O. Box 32, El Alia, Bab ezzouar, Algiers, Algeria

    Merouane Bouzid & Salah-Eddine Cheraitia

Authors
  1. Merouane Bouzid
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  2. Salah-Eddine Cheraitia
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Corresponding author

Correspondence to Merouane Bouzid .

Editor information

Editors and Affiliations

  1. SPIIRAS, Saint-Petersburg, Russia

    Andrey Ronzhin

  2. Moscow State Linguistic University, Moscow, Russia

    Rodmonga Potapova

  3. University of Patras, Patras, Greece

    Nikos Fakotakis

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© 2015 Springer International Publishing Switzerland

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Bouzid, M., Cheraitia, SE. (2015). Voicing-Based Classified Split Vector Quantizer for Efficient Coding of AMR-WB ISF Parameters. In: Ronzhin, A., Potapova, R., Fakotakis, N. (eds) Speech and Computer. SPECOM 2015. Lecture Notes in Computer Science(), vol 9319. Springer, Cham. https://doi.org/10.1007/978-3-319-23132-7_58

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  • DOI: https://doi.org/10.1007/978-3-319-23132-7_58

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-23131-0

  • Online ISBN: 978-3-319-23132-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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