An autocorrelation measure of a single-frame which is not derived directly from the data is defined. A lattice method is first used to compute the frame-reflection coefficients. Then the predictor coefficients are computed, and the normal equations result in a new set of autocorrelation lags named the lattice autocorrelation measure (LAM). LAM is shown to be more accurate than the conventional Bartlett autocorrelation measure, especially in case of few samples and low SNR. The application of LAM in eigenvalues spectral analysis gives better estimates than conventional autocorrelation, mainly in the central frequency range. The use of the LAM in overdetermined autocorrelation matrices analyzed by singular value decomposition leads to an improvement in the frequency estimation of very close harmonics in the cases of short signal frames.