AbstractDistance sampling is widely used to estimate animal population densities by accounting for imperfect detection of individuals with increasing distance from an observer. Distance sampling assumes that distances are measured without error; however, it is often applied to human estimated distances, which are known to be inconsistent, inaccurate, and biased. We present an objective technique for estimating distance to vocalizing individuals that relies on the relative sound level (RSL) of the vocalization extracted from autonomous recording unit (ARU) recordings and show the error is less than human estimated error extracted from a literature case study.RSLpredicted distances can be obtained by manual measurement in sound viewing software, or automatically with automated signal recognition software. We built calibration datasets of Ovenbirds (Seiurus aurocapilla) and Common Nighthawks (Chordeiles minor) recorded at known distances and used regression ofRSLfrom those recordings to predict distance. There was no error bias ofRSLpredicted distances when compared to known distances for Common Nighthawk, minimal error bias for Ovenbird, and error from allRSLpredicted distances was less than human estimated error extracted from the literature. We then simulatedARUpoint count surveys with a known density and estimated that density with distance sampling to test whetherRSLdistance prediction does not violate the assumption that distances are measured without error. There was no difference in density estimates from known distance and density estimates obtained fromRSLpredicted distance, while density estimates contaminated with human estimated error were significantly lower than density estimates from known distance. We found that a calibration dataset of approximately 300 vocalizations was suitable to minimize error for both species, and so conclude thatRSLdistance prediction is an accessible method of improving distance estimates relative to human estimation. We provide general recommendations on how to collect calibration recordings for the application ofRSLdistance prediction to other species and areas.