Linear predictive coding with modified filter structures

In conventional one-step forward linear prediction, an estimate for the cur rent sample value is formed as a linear combination of previous sample valu es. In this paper, a generalized form of this scheme is studied. Here, the prediction is not based simply on the previous sample values but to the sig nal history as seen through an arbitrary filterbank. It is shown in the pap er how the coefficients of a modified model can be obtained and how the inv erse and synthesis filters can be implemented. Various properties of such s ystems are derived in this article. As an example, a novel linear predictiv e system using inherently logarithmic frequency representation is introduce d.