Frequency-warped signal processing techniques are attractive to many wideband speech and audio applications since they have a clear connection to the frequency resolution of human hearing. A warped version of linear predictive coding (LPC) is studied in this paper. The performance of conventional and warped LPC algorithms are compared in a simulated coding system using listening tests and conventional technical measures. The results indicate that the use of warped techniques is beneficial especially in wideband coding and may result in savings of one bit per sample compared to the conventional algorithm while retaining the same subjective quality.

Considered are general, pure linear prediction schemes, where the prediction of the current sample is based on IIR-filtered past samples. Properties of these schemes are discussed, in particular, the whitening property, the realisation of the synthesis filter and its stability. The realisation problem as occurring in warped linear prediction is solved. Furthermore, it is proved that, at least for a specific class of systems, data input windowing for the calculation of the optimal prediction coefficients guarantees the stability of the synthesis filters.

Autoregressive (AR) modeling by linear prediction (LP) provides the basis of a wide variety of signal processing and communication systems including parametric spectral estimation and system identification. Perhaps the greatest success of linear prediction techniques is to be found in speech analysis and audio coding. In this paper, we first reviewed the general frameworks of predictive signal modeling and investigated various prediction filter structures including the modified linear predictor. We then empirically compared the compression performamce of these prediction filters by applying to the lossless audio compression system. We also applied different filter orders and block lengths for each filter to explore their influence on the compression ratio....