Abstract-This paper presents an adaptive algorithm for the restoration of lost sample values in discrete-time signals that can locally be 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 established. The method has been tested on artificially generated autoregressive processes as well as on digitized music and speech signals. described by means of autoregressive processes. The only restrictions sk t T
REF proposed an adaptive interpolation method to restore lost data, with the restrictions that the positions of the unknown samples are known.
Adaptive interpolation of discrete-time signals that can be modeled as autoregressive processes
IEEE Trans. Acoustics, Speech and Sig. Proc
Mathematics Computer Science