Crepitus vibrational and acoustic signal analysis of the human facet joints of the lumbar spine has historically been a difficult problem due to the inhomogeneous and varied signal characteristics. Although previous studies have examined tribological characteristics of facet joints, we have found vibrational and acoustic signals to be a satisfactory analog to facet joint tribology and function. Here, we improve upon our previous automated computational method, now enhancing it for the analysis of human crepitus. Compared with this group’s previous studies using a mechanical model, human crepitus is extremely complex. Therefore, we proposed an automated method (AM) of analysis that used a test set (n = 16) and an experimental set of data (n = 48). We had a fair level of interrater agreement (Kw = 0.367, standard error (SE) = 0.054, 95% confidence interval (CI) = 0.260–0.474) between the AM and human observers before adjustments were made in the AM. Following adjustments to the AM, interrater agreement improved to a substantial level (Kw = 0.788, SE = 0.056, 95% CI = 0.0.682–0.895). In the future, we recommend a machine learning study with a larger number of subjects who can better capture the nuances of varying types of human crepitus.