Forward Adaptation of Novel Semilogarithmic Quantizer and Lossless Coder for Speech Signals Compression

  • Authors:

  • Zoran H. Peric;Milan S. Savic;Milan R. Dincic;Dragan B. Denic;Momir R. Prascevic

  • Affiliations:

  • Faculty of Electronic Engineering, University of Nis, Aleksandra Medvedeva 14, 18000 Nis, Serbia, e-mail: zoran.peric@elfak.ni.ac.rs;Faculty of Electronic Engineering, University of Nis, Aleksandra Medvedeva 14, 18000 Nis, Serbia, e-mail: zoran.peric@elfak.ni.ac.rs;Faculty of Electronic Engineering, University of Nis, Aleksandra Medvedeva 14, 18000 Nis, Serbia, e-mail: zoran.peric@elfak.ni.ac.rs;Faculty of Electronic Engineering, University of Nis, Aleksandra Medvedeva 14, 18000 Nis, Serbia, e-mail: zoran.peric@elfak.ni.ac.rs;Faculty of Occupational Safety, University of Nis, Carnojevica 10 A, 18000 Nis, Serbia

  • Venue:
  • Informatica
  • Year:
  • 2010

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Abstract

In this paper new semilogarithmic quantizer for Laplacian distribution is presented. It is simpler than classic A-law semilogarithmic quantizer since it has unit gain around zero. Also, it gives for 2.97 dB higher signal-to-quantization noise-ratio (SQNR) for referent variance in relation to A-law, and therefore it is more suitable for adaptation. Forward adaptation of this quantizer is done on frame-by-frame basis. In this way G.712 standard is satisfied with 7 bits/sample, which is not possible with classic A-law. Inside each frame subframes are formed and lossless encoder is applied on subframes. In that way, double adaptation is done: adaptation on variance within frames and adaptation on amplitude within subframes. Joined design of quantizer and lossless encoder is done, which gives better performances. As a result, standard G.712 is satisfied with only 6.43 bits/sample. Experimental results, obtained by applying this model on speech signal, are presented. It is shown that experimental and theoretical results are matched very well (difference is less than 1.5%). Models presented in this paper can be applied for speech signal and any other signal with Laplacian distribution.