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memse transforms a real-valued time series (from the specified input-file, or from the standard input if input-file is specified as ‘‘-’’; input-file must be in text form) into a power spectrum (on the standard output). memse is designed to be used in much the same way as fft(1) ; it accepts the same input, produces output in the same format, and accepts many of the same options used with fft.
Unlike fft, which bases its spectral estimates on the discrete Fourier transform, memse uses the maximum entropy (all poles) method, also known as autoregressive (AR) spectral estimation. This method models the spectrum by a series expansion in which the free parameters are all in the denominators of its terms; hence each term may represent a pole (corresponding to infinite power spectral density within an infinitely narrow frequency band). By contrast, Fourier analysis models the spectrum by a series expansion in which the free parameters are all in the numerators; hence each term in a Fourier series may represent a zero. All-poles models are particularly useful for analysis of spectra which have discrete peaks (in the terminology of optical spectra, ‘‘lines’’).
In order to use memse, you should have some idea of the order of the model you wish to use (i.e., the number of poles). Although this may be any number up to the number of input points, the number of poles generally should not exceed the square root of the number of input points, and usually should be considerably less than that number. Large numbers of poles lead to lengthy computations (much slower than the FFT) in which accumulated roundoff error becomes a serious problem. This problem may also occur if the length of the input series becomes excessive. The recommended way to use memse is to begin by using fft, in order to estimate the model order. Typically this should be a small multiple of the number of peaks which you believe are present. Beware! memse will produce smooth spectral estimates for whatever model order you choose -- and they may be totally bogus if you choose incorrectly. Varying the model order can help to weed out some spurious features, but use extreme care when interpreting memse output given noisy input.
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Updated 28 November 2018