N-stage predictive feedback-based compression and decompression of spectra of stochastic data using convergent incomplete autoregressive models

摘要

The spectral range of a stochastic time series of information, including unvoiced speech is reduced to allow transmission over a substantially narrowed frequency band. Sets of autoregressive (AR) parameters are identified for successive time windows of the original time series and of subsequent stages of subsampled reduced-spectrum models of each window of the original time series are used. The AR parameters are transmitted together with subsampled windows of the original data. These AR parameters are used to reconstruct a least square stochastic estimate of the transmitted subsampled time series in a backwards manner from the most subsampled spectrum back to the original spectrum using a sequence of predictive feedback algorithms. Past prediction outputs are feedback for prediction whenever samples are missing. This process yields a high quality reconstructed signal that preserves not only speech parameters and intelligibility, but also near- natural speaker identifiability.