  Periodicity estimation from an arbitrary length noisy signal is
computationally very costly. A recently developed Ramanujan Fat Dictionary is
one of the ways to find the hidden components from an arbitrary length (non
integral multiple of period) of the signal. This method suffers from high run
time due to the lack of information about the period and effect of noise on the
signal. We propose a new method that efficiently estimates the period of the
signal and finding the hidden components thus becomes easy from it. Our method
works well with significantly low SNR values and runs in O(n) time complexity,
n being length of the signal. Comparision of run time analysis between our
method for period estimation of a given signal and SVD method at various SNR
values has been made and the corresponding hidden components are there by
extracted by projecting onto the factor-Ramanujan Subspaces.
