By Frank Fahy, John Walker
Complicated functions in Noise and Vibration offers remedies of acoustic and vibration phenomena including summaries of the newest wisdom, research and strategies at present to be had for facing functional difficulties.
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Extra info for Advanced Applications in Acoustics, Noise and Vibration
The most widely considered technique is based on exploiting the key difference between auto-terms and cross-terms, namely, that cross-terms oscillate and auto-terms commonly have slowly varying amplitudes. Alternatively, if the WVD is viewed as an image, the cross-terms are associated with high spatial frequencies and the auto-terms occupy the low spatial frequency. By applying a two-dimensional low-pass filter to the WVD one can reduce the cross-terms, but in doing so one also smears sharp lines resulting in a loss of resolution.
This section describes some of these methods which are variously described as ‘model based’ or ‘high resolution’. 1 29 Time series models (Gutowski et al. 1978; Priestley 1988) It seems self-evident that the procedure used to estimate a spectrum of a time series should depend upon the process by which the data are generated. If the mechanism of data generation is unknown, then there is no one ‘correct’ method of spectral estimation. , and various models have been proposed to account for the behaviour.
We emphasise that the discussions so far have related to the definition of the MEM spectrum using exact values of the autocovariance function. No mention has been made of the use of estimates of the autocovariance function to produce estimates of the MEM spectra. The estimates of the autocovariance function contain the effects of additive noise and data truncation. The first problem was considered by Ables (1974) who suggested the modification that the autocorrelation be constrained to pass close to measured values.