Detecting noise in a time series.
A numerical algorithm is presented for estimating whether, and roughly to what extent, a time series is noise corrupted. Using phase-randomized surrogates constructed from the original signal, metrics are defined which can be used to quantify the noise level. A saturation occurs in these metrics at signal to noise ratios (SNRs) of around 0 dB and below, and also at around 20 dB and above. In between these two regions there is a monotonic transition in the value of the metrics from one region to the other corresponding to changes in the SNR. (c) 1997 American Institute of Physics.
Cellucci
C. J.
CJ
Department of Physics, Bryn Mawr College, Bryn Mawr, Pennsylvania 19010.
Albano
A. M.
AM
Rapp
P. E.
PE
Pittenger
R. A.
RA
Josiassen
R. C.
RC