A geospatial model of the global ambient soundscape

Humans and nature form an intricately coupled system with the ambient soundscape: anthropogenic, biological, and geophysical sources produce the sounds that comprise the soundscape, and, in turn, the ambient sound level affects the behavior and well-being of humans and animals. To assess the impact of the soundscape on both humans and animals, it is necessary to understand how the ambient sound level varies in space and time. A model for the ambient sound level was developed based on an ensemble of machine learning algorithms, which were trained using more than one million hours of ambient acoustic measurements acquired at hundreds of geospatially diverse locations across the United States. The resulting model predicts the ambient sound level based on geospatial features such as nighttime lights, land cover, population, climate, topography, hydrology, and transportation. A database of geospatial features with worldwide coverage was created, and the model was applied to predict the time-varying ambient sound level across the entirety of Earth’s land surface. Furthermore, the relative contributions of anthropogenic and natural sources to the soundscape were estimated by artificially changing the values of various geospatial features and reapplying the model. [Work funded by an Army SBIR.]