Analysis of the Instantaneous Estimate of Autocorrelation

Introduction: The Problem • Many signals are not stationary of the second order (their Autocorrelation Function (ACF) is time-varying) • The ACF is used to develop filters and predictors (e.g. for signal compression) • This suggests that we need filters and predictors that are time-varying • A common approach has been to assume local stationarity: We can estimate filters for the signal by using ‘frame blocking’ Frame blocking does not necessarily identify appropriate frame boundaries Introduction: A Potential Solution • It would be better if we could design filters which changed along with the autocorrelation of the underlying data • The Instantaneous Estimate of Autocorrelation (IEAC) is a time-varying estimate of the autocorrelation of a signal • Thus, we should vary our filters according to variation in the IEAC • We concentrate on analysis of the IEAC, and leave the development of filters based on this signal to a forthcoming paper