Characterization of Ambient Noise
Abstract
An Air Force sponsor is interested in improving an acoustic detection model by providing better estimates to characterize the background noise of various environments. This would help inform decision makers on the probability of acoustic detection of different systems of interest given different levels of noise. Data mining and statistical learning techniques are applied to a National Park Service acoustic summary data set to nd overall trends over varying environments. Linear regression, conditional inference trees, and random forest techniques are discussed. Findings indicate only sixteen geospatial variables at different resolutions are necessary to characterize the first ten 1/3 octave band frequencies of the L90 band using just the linear regression. The accuracy of the regression model is within 2 to 6 decibels and depends on the frequency of interest. This research is the first of its kind to apply linear regression to the national park service acoustic dataset, and second to apply random forests for predicting noise levels. Future research is needed to determine the accuracy of the model when applied outside of the national park service in intended Air Force operational environments.
Document Details
- Document Type
- Technical Report
- Publication Date
- Mar 22, 2018
- Accession Number
- AD1056413
Entities
People
- Rachel C. Ramirez
Organizations
- Air Force Institute of Technology