The Kubo lineshape is a common model of spectral diffusion in frequency fluctuation correlation functions (FFCFs) owing to its simple, closed-form expression and flexibility to describe the limiting cases of homogeneous and inhomogeneous dephasing. As the FFCF is not a direct experimental observable, a variety of approximate metrics have been used to extract an approximate FFCF from 2D frequency resolved line shapes in two-dimensional infrared (2D IR) spectra. Some of these approaches include the nodal-line slope, dynamic linewidth, ellipticity, covariance, spectral phase slope, inhomogeneity index, eccentricity, center line slope (CLS), inverse center line slope (invCLS), and the correlation coefficient obtained by a 2D Gaussian fit. The most popular among these has been the CLS, which, relative to some other metrics, is more reliable at lower SNR, is invariant to lineshape interference from anharmonic peaks or phase twist, and is invariant to apodization time for time constants and relative amplitudes. There are, however, several shortcomings of the CLS method, which are also characteristics of most of the other common metrics. First, it is unreliable for characterizing relatively fast processes due to the short-time approximation, which, following second-order cumulant expansion, ignores dephasing during coherence times. Second, it requires a second step of fitting to the linear absorbance spectrum to obtained absolute values for Kubo amplitudes and homogeneous dephasing. This fitting is problematic in situations where the linear absorption is either unavailable or unreliable due to dilute reporters, weak extinction coefficients, or spectral congestion. Furthermore, linear absorption spectra are often inaccurate near the baseline, which can distort the resulting fit parameters. Third, absolute values of Kubo amplitudes and homogeneous dephasing are sensitive to the apodization time, meaning results may vary depending on the duration of coherence time measured or how quickly the apodization filter tapers to zero. Fourth, the CLS method still requires a remarkably high SNR (e.g. ~100:1) to yield reliable results.