Abstract
This chapter studies the modelling of quantization noise in a predictive coding algorithm. The specific algorithm is a low-delay, vector-predictive transform coder for speech signals. The coder uses a Kaiman filter with a backward estimated LPC model for the speech signal combined with an additive noise model for the quantization noise. Three different approaches to the quantization noise problem are described and compared. These are: neglecting the quantization noise in the prediction, modelling it as an uncorrelated additive noise source, and modelling it as a noise source correlated with the quantizer input. Simulations indicate that this last approach is the most efficient among those described. Moreover, we study the choice of measurements for the Kaiman filter in connection with signal smoothing. In the coding algorithm described, the measurements are obtained by a transform matrix. Simulations indicate that an overlap in the vectors, which are transformed, provides an improvement in coder performance.
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Andersen, S.V., Jensen, S.H., Hansen, E. (1998). Vector-Predictive Speech Coding with Quantization Noise Modelling. In: Procházka, A., Uhlíř, J., Rayner, P.W.J., Kingsbury, N.G. (eds) Signal Analysis and Prediction. Applied and Numerical Harmonic Analysis. Birkhäuser, Boston, MA. https://doi.org/10.1007/978-1-4612-1768-8_30
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DOI: https://doi.org/10.1007/978-1-4612-1768-8_30
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