Adaptive interpolation of discrete-time signals that can be modeled as autoregressive processes
- 1 April 1986
- journal article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Acoustics, Speech, and Signal Processing
- Vol. 34 (2) , 317-330
- https://doi.org/10.1109/tassp.1986.1164824
Abstract
Are that the positions of the unknown samples should be known and that they should be embedded in a sufficiently large neighborhood of known samples. The estimates of the unknown samples are obtained by minimizing the sum of squares of the residual errors that involve estimates of the autoregressive parameters. A statistical analysis shows that, for a burst of lost samples, the expected quadratic interpolation error per sample converges to the signal variance when the burst length tends to infinity. The method is in fact the first step of an iterative algorithm, in which in each iteration step the current estimates of the missing samples are used to compute the new estimates. Furthermore, the feasibility of implementation in hardware for real-time use is es- tablished. The method has been tested on artificially generated auto- regressive processes as well as on digitized music and speech signals. described by means of autoregressive processes. The only restrictions sk tKeywords
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