The ability to accurately characterize the soundscape, or combination of sounds, of diverse geographic areas has many practical implications. Interested parties include the United States military and the National Park Service, but applications also exist in areas such as public health, ecology, community and social justice noise analyses, and real estate. I use an ensemble of machine learning models to predict ambient sound levels throughout the contiguous United States. Our data set consists of 607 training sites, where various acoustic metrics, such as overall daytime L50 levels and one-third octave frequency band levels, have been obtained. I have data for 117 geospatial features for the entire contiguous United States, which include metrics such as distance to the nearest road or airport, and the percentage of industrialization or forest in a specific area. I discuss initial model predictions in the spatial, frequency, and temporal domains, and the statistical advantages of using an ensemble of machine learning models, particularly for limited data sets. I comment on uncertainty quantification for machine learning models originating from limited data sets.
College and Department
Physical and Mathematical Sciences; Physics and Astronomy
BYU ScholarsArchive Citation
Pedersen, Katrina Lynn, "Using Machine Learning to Accurately Predict Ambient Soundscapes from Limited Data Sets" (2018). Theses and Dissertations. 9272.
acoustics, ensemble model, machine learning, soundscape, statistics, uncertainty quantiﬁcation