title: Detecting noise in a time series.
abstract: 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.
authors.affiliation: Department of Physics, Bryn Mawr College, Bryn Mawr, Pennsylvania 19010.
authors.forename: C. J.
authors.initials: CJ
authors.lastname: Cellucci
authors.forename: A. M.
authors.initials: AM
authors.lastname: Albano
authors.forename: P. E.
authors.initials: PE
authors.lastname: Rapp
authors.forename: R. A.
authors.initials: RA
authors.lastname: Pittenger
authors.forename: R. C.
authors.initials: RC
authors.lastname: Josiassen