Many researchers base their findings upon acoustic features of vocalisms, found by visually inspecting ("eyeballing") spectrograms, or by reading off numbers from software tools without knowing to what signal-processing standard those numbers were determined. Without using a consistent measuring tape, so to speak, the scientific process of reproducing and confirming others' results becomes more problematic, and measurement itself becomes open to bias. We document our improved signal processing algorithms to more accurately determine spectral parameters from vocalisms, in an effort to achieve more precise, reliable and consistent results. We propose a dynamic weighted-harmonic scaling algorithm to measure fundamental frequency. Furthermore, new methods are created to account for frequency modulation in vocalisms, such as periodic warbling and linear chirping. New vocal measurement features become possible, as a result of the new methods. For example, several novel variants of maximum/minimum fundamental frequency, peak frequency, and bandwidth, become available to the researcher. Novel vocal features provide researchers with new possibilities in linking vocalisms to behaviour. Recorded vocalizations of ring-tailed lemurs (Lemur catta) were fed into the algorithms, and the results are presented in comparison to results from traditional analysis techniques. New vocal features made possible by the new methods are correlated with other data including body mass, and their degrees of correlation are presented in order to contrast how correlation-informative the new methods are compared to traditional methods. Current work is presented in creating a freely accessible software program for researchers to apply these methods to their own vocal recordings of primates, as well as other organisms.