Abstract
This article investigates switched quantizers for speech signal depicted with Gaussian probability density function (PDF). Gaussian PDF is better for smaller frame lengths that are represented here. Companding technique results in constant Signal to Quantization Noise Ratio (SQNR). In this paper two approaches are present: quasi-logarithmic (QL) and piecewise uniform (PU) compandor. Simpler compandor directly affects the complexity of hardware realization and expense of given solution, but, on the other hand, also brings to weaker performances. Therefore, a smart choice has to be made. Usage of switched technique leads to better performances. This way, the quality of quantization is improved by dividing the dynamic range of variances into multiple subranges. For each subrange a separate quantizer is designed, with its support region’s amplitude. The optimal amplitude is numerically determined, whereby a single criterion is obtaining the maximal SQNR. Bit rates of these quantizers don’t depend on signal variance, as the fixed length codes are used. The performances of proposed quantizers are demonstrated on real speech signals from the reliable database. Comparison of obtained results with other recent solutions is done in order to show the efficiency of this model.DOI: http://dx.doi.org/10.5755/j01.eie.24.6.22295
Highlights
The design of quantizers for speech signal transmission mostly assumes that input signal can be well described with Gaussian or Laplacian probability density functions (PDF)
A quantizer designed according to the Gaussian PDF leads to better performances, i.e. higher Signal to Quantization Noise Ratio (SQNR), than a quantizer with Laplacian PDF
Since this is the case in our paper, we will present a model with Gaussian PDF
Summary
The design of quantizers for speech signal transmission mostly assumes that input signal can be well described with Gaussian or Laplacian probability density functions (PDF). Both approaches are represented in the recent researches [1]–[4]. A quantizer designed according to the Gaussian PDF leads to better performances, i.e. higher Signal to Quantization Noise Ratio (SQNR), than a quantizer with Laplacian PDF. Another approach is a frame size: the quantizer with Gaussian PDF is used for smaller frames [5]. In order to prove the efficiency of proposed solutions, we performed their software simulations on real speech signals from [7]
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