Nonlinear adaptive prediction of nonstationary signals with application to speech coding

The purpose of this contribution is to present a new approach for the prediction of speech signals that is appropriate to speech coding. The procedure is based upon the principles of blind equalisation. In an earlier publication we examined these principles from the prediction point of view as a general method. The present contribution examines the approach in relation to speech signal representation for coding and compression. The method outlined in this contribution offers significant advantages compared to the standard prediction methods. This is because the signal estimation is carried out on a sample by sample basis, it needs no estimation of the covariance matrix or some other long term statistical attributes, it makes no assumption on a minimum phase vocal tract transfer function, and hence, it is faithful to the nonstationarities in the analysed signal. The method can be seen as mapping a given signal into a set of signals of increased correlational properties, which in turn may be so mapped until the signals exhibit piecewise constant behaviour. At this stage they are easily modelled and the reverse process can be put into effect for the original signal reconstruction. Speech signals can be rendered piecewise constant approximately after some signal decompositions. Then, the percentage error of predicted value is involved to examine if the decomposition stops. Examples illustrating these principles are included.