  Periodicity analysis of unevenly collected data is a relevant issue in
several scientific fields. In astrophysics, for example, we have to find the
fundamental period of light or radial velocity curves which are unevenly
sampled observations of stars. Classical spectral analysis methods are
unsatisfactory to solve the problem. In this paper we present a neural network
based estimator system which performs well the frequency extraction in unevenly
sampled signals. It uses an unsupervised Hebbian nonlinear neural algorithm to
extract, from the interpolated signal, the principal components which, in turn,
are used by the MUSIC frequency estimator algorithm to extract the frequencies.
The neural network is tolerant to noise and works well also with few points in
the sequence. We benchmark the system on synthetic and real signals with the
Periodogram and with the Cramer-Rao lower bound.
