Two-sided filters for frame-based prediction
A linear prediction model, based on a two-sided predictor which predicts on the basis of past and future samples within a frame, is presented. The linear prediction model may be applied wherever frame-based prediction is used. A stable synthesis procedure is derived by casting the prediction equation as a cyclic convolution in the time domain. When the filter order is the maximum possible, the synthesis filter is shown to have a frequency response proportional to the squared magnitude of the DFT of the frame. A symmetric two-sided predictor which has only half the number of coefficients to be coded as compared to a one-sided predictor of the same order is described. Two-sided prediction showed at least 5-dB improvement in prediction gain over one-sided prediction in simulations on speech data. >