A nonlinear adaptive predictor for speech compression

A neural nonlinear predictor for one dimensional signals is presented. It is based on a combination of linearization and QR decomposition that allows a fast adapting algorithm. The predictor is used in a speech compression algorithm that has proven to be superior to linear based models. The compression and training are done simultaneously, allowing the network to continually adapt to the signal. The results presented show that this algorithm outperforms a typical LPC coding algorithm.