Quantization Properties of Transmission Parameters in Linear Predictive Systems

摘要

Several alternate sets of parameters that represent the linear predictor are investigated as transmission parameters for linear predictive speech compression systems. Although each of these sets provides equivalent information about the linear predictor, their properties under quantization are different. The results of a comparative study of the various parameter sets are reported. Specifically, it is concluded that the reflection coefficients are the best set for use as transmission parameters. A more detailed investigation of the quantization properties of the reflection coefficients is then carried out using a spectral sensitivity measure. A method of optimally quantizing the reflection coefficients is also derived. Using this method it is demonstrated that logarithms of the ratios of the familiar area functions possess approximately optimal quantization properties. Also, a solution to the problem of bit allocation among the various parameters is presented, based on the sensitivity measure. The use of another spectral sensitivity measure renders logarithms of the ratios of normalized errors associated with linear predictors of successive orders as the optimal quantization parameters. Informal listening tests indicate that the use of log area ratios for quantization leads to better synthesis than the use of log error ratios. (Author)