Noisy animal communication presents a unique challenge for biologists interpreting a produced signal precisely and accurately. One past approach has been to reduce noise. Lately, with the advance of quantitative tools, an alternative is to use the noise to improve model predictions. Here we take the latter approach to improve our ability to recover honey bee (Apis mellifera spp.) foraging locations, in particular the distance at which they forage, from observed waggle dance communications, which encode the vector from the hive to the forage. This vector gives both the distance from the hive to the forage, which is encoded in dance duration, and the direction from the hive to the forage, which is encoded in the angle that the bee makes while dancing. We analysed the waggle dances from individually marked honey bees (N = 859 dances from 85 bees from 3 hives) that foraged at multiple known locations to determine a distance-to-duration calibration. Next we compared this calibration to a previously published calibration, which demonstrated that while their slopes were similar (P = 0.82), their intercepts differed (P < 0.001); however, the individual variation, or noise, between bees was so high that this difference was rendered biologically irrelevant. We then collated our data with all published calibration studies to generate a universal calibration that incorporates the interindividual and interstudy differences. Lastly, we verified that the universal calibration performed as well as a landscape/subspecies-specific calibration, first with a hold-out sample of waggle dances (N = 84) (performance comparison: P = 0.36) and second with a linear discriminant analysis, which failed to assign dances to their originating population. Both results confirm that the universal calibration may be used, irrespective of landscape or subspecies. Therefore, by allowing noise to be part of our analysis, the usability of our calibration is broadened for use in future settings.