Abstract
Ambient soundscape data are a critical component of numerous applications such as evaluating the environmental noise impacts of proposed activities and studying the physical and psychological impacts of a person’s acoustic environment. The current approach to characterize ambient soundscapes requires intensive field measurement programs. A soundscape prediction tool capable of modeling the soundscape over large spatial regions and timeframes is needed to provide an alternative to intensive field measurements. To address this need, a machine learning based soundscape model trained with acoustic data and geospatial layers has been developed. The model’s capability to generate A and flat-weighted exceedance levels across space, time (hourly, daytime, and nighttime), and frequency (one-third octave bands) will be demonstrated. [Work funded by an Army SBIR.]
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