Abstract

When creating data-based models it is important to include the underlying physical characteristics and constraints of the data. If physical characteristics are not properly included in the model, results may be infeasible or physically impossible. Acoustic environments are better characterized by ensuring that models include the fundamental spatial, spectral, and temporal characteristics of noise sources, or how they change based on location, frequency, and time. When model data are limited, in availability or in reliability, additional care must be taken to ensure models predict feasible results. This dissertation focuses on physics-guided modeling of acoustic environments using limited data, taking into consideration spatial, spectral, and temporal characteristics of noise sources, specifically focused on wind noise and traffic noise. Wind noise contamination in spectral data can be significant, even when using a windscreen. By modeling spectral characteristics of temporally varying wind noise contamination, a method for automatically detecting and reducing wind noise was developed. Reducing non-acoustic wind noise contamination allows for better characterization of outdoor acoustic environments and is useful for accurately measuring other noise sources. Traffic noise varies spatially, spectrally, and temporally, and depends on traffic volume (the number of vehicles per unit time) and traffic class mix (e.g., the relative number of small vehicles compared to large trucks). Using the temporal variation found in reported traffic volume at thousands of locations, a model was developed to represent and predict the spatio-temporal variability of traffic volume nationwide. Further models were created to include dynamic changes in traffic class mix and to predict spectral source traffic noise. The resulting model for predicting source traffic noise is known as VROOM, the Vehicular Reduced-Order Observation-based Model. The physics-guided modeling techniques presented in this dissertation are intended for characterizing acoustic environments, which has applications for such diverse areas as human health and wellness, bioacoustics, wildlife conservation, urban and roadway planning, land development and conservation, noise ordinance legislation, homebuying, and more.

Degree

PhD

College and Department

Physics and Astronomy; Computational, Mathematical, and Physical Sciences

Rights

https://lib.byu.edu/about/copyright/