  We present a new approach to solve the exponential retrieval problem. We
derive a stable technique, based on the singular value decomposition (SVD) of
lag-covariance and crosscovariance matrices consisting of covariance
coefficients computed for index translated copies of an initial time series.
For these matrices a generalized eigenvalue problem is solved. The initial
signal is mapped into the basis of the generalized eigenvectors and phase
portraits are consequently analyzed. Pattern recognition techniques could be
applied to distinguish phase portraits related to the exponentials and noise.
Each frequency is evaluated by unwrapping phases of the corresponding portrait,
detecting potential wrapping events and estimation of the phase slope.
Efficiency of the proposed and existing methods is compared on the set of
examples, including the white Gaussian and auto-regressive model noise.
