  As an old and widely used tool, it is still possible to find new insights and
applications from Fast Fourier Transform (FFT)-based analyses. The FFT is
frequently used to generate the Power Spectral Density (PSD) function, by
squaring the spectral components that have been corrected for influence from
the instrument that generated the data. Although better than a raw-data
spectrum, by removing influence of the instrument transfer function, the PSD is
still of limited value for time varying signals with noise, due to the very
nature of the Fourier transform. The authors present here another way to treat
the FFT data, namely the Cumulative Spectral Power (CSP), as a promising means
to overcome some of these limitations. As will be seen from the examples
provided, the CSP holds promise in a variety of different fields.
