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A common set of signal features measurable by a basic sound level meter are analyzed, and the quality of information carried in subsets of these features are examined for their ability to discriminate military blast and non-blast sounds. The analysis is based on over 120 000 human classified signals compiled from seven different datasets. The study implements linear and Gaussian radial basis function (RBF) support vector machines (SVM) to classify blast sounds. Using the orthogonal centroid dimension reduction technique, intuition is developed about the distribution of blast and non-blast feature vectors in high dimensional space. Recursive feature elimination (SVM-RFE) is then used to eliminate features containing redundant information and rank features according to their ability to separate blasts from non-blasts. Finally, the accuracy of the linear and RBF SVM classifiers is listed for each of the experiments in the dataset, and the weights are given for the linear SVM classifier.

Variability in received sound levels were investigated at distances ranging from 4 m to 16 km from a typical blast source in two locations with different climates and terrain. Four experiments were conducted, two in a temperate climate with a hilly terrain and two in a desert climate with a flat terrain, under a variety of meteorological conditions. Sound levels were recorded in three different directions around the source during the summer and winter seasons in each location. Testing occurred over the course of several days for each experiment during all 24 h of the day, and meteorological data were gathered throughout each experiment. The peak levels (L(Pk)), C-weighted sound exposure levels (CSEL), and spectral characteristics of the received sound pressure levels were analyzed. The results show high variability in L(Pk) and CSEL at distances beyond 2 km from the source for each experiment, which was not clearly explained by the time of day the blasts occurred. Also, as expected, higher frequency energy is attenuated more drastically than the lower frequency energy as the distance from the source increases. These data serve as a reference for long-distance blast sound propagation.

Current blast noise assessment procedures at military installations in the United States do not fully meet the military s noise management needs; military blast noise sometimes disturbs surrounding communities, resulting in legal actions against US military installations. Specifically, current procedures do not accurately capture the way humans respond to blast events, and do not adequately account for the level, number, timing, and spatial variability of blast noise events. This work constructed and administered the General Community Survey (GCS) within SERDP Project WP-1546 at the first of three military installations to determine how blast noise levels affect general community annoyance and how the community reaction changes over time in response to a dynamic blast noise environment. The results indicate that, while blast noise was the most annoying noise source around this installation, current blast noise assessment metrics are weakly correlated with community annoyance, and a large percentage of the study population were highly annoyed at relatively low C-weighted Day-Night blast noise levels. Current findings highlight the importance of capturing temporal and spatial variation of the both stimulus and response, and also of non-acoustical factors such as habituation and vibration.

Unattended and continuously running environmental noise monitoring systems can capture an intractable amount of data. The signals captured can include a multitude of sources (e.g., wind noise and anthropogenic noise sources) in addition to the environmental noise sources of interest (e.g., aircraft, vehicles, trains, and military weapons). In this presentation, we explore the use of machine-learning methods to effectively isolate and identify environmental noise sources captured on such a noise monitoring system. Specifically, we consider the use of both unsupervised (e.g., principle components analysis, clustering methods, and deep belief networks) and supervised (e.g., logistic regression, support vector machines, and neural networks) pattern-learning methods to derive the features of interest and classify the signals based on the obtained features. The generalization performance of each method is assessed using a dataset of over 120,000 human classified signals, and the strengths and weaknesses of each approach are discussed.

Outdoor sound propagation is highly dependent upon meteorological conditions. While this, of course, is a trivial statement, predicting sound levels based on meteorology is not. This is especially true for signals that propagate many kilometers, as is the case for those generated by high-energy impulsive sources such as explosions and heavy weaponry; waves have ample opportunity for refraction by and scattering from local atmospheric features along the entire propagation path. The range of received blast levels at distances greater than 2 km can span nearly 50 dB, depending on weather conditions. Using a statistical learning method known as a Random Forest, we demonstrate the prediction of levels from simple meteorological measurements in the face of this extreme variability. With simple, spatially sparse meteorological data, the model can predict levels to within 3 dB at 2 km and 5 dB at 15 km. The results presented here suggest that as more data are acquired through continuous noise monitoring programs, physics-blind, data-driven statistical models have the potential to supplant computationally intensive propagation models for noise prediction. Caveats and cautions when using these types of machine learning methods will also be discussed.

It is common for residents living on and around military installations to be exposed to a significant amount of high amplitude impulsive noise, primarily from large weaponry and other blast noise producing sources. Yet in comparison to transportation noise, there have been relatively few studies of how communities and individuals respond to this type of noise. This presentation will report the latest findings from recent human response to blast noise studies conducted at three military installations. Across all sites, blast noise has been found to be the most annoying noise source, despite the fact that a large percentage of respondents reported that their neighborhood was a good or excellent place to live. It has also been found that each community and individual has a unique tolerance to blast noise. Furthermore, individuals use a different and finite portion of the response scale, suggesting that the current methodology of fixating on the percent of the population that is highly annoyed may inadvertently be discarding useful response information. Comparisons between respondents living on- and off-post within and between study sites will be made, with special emphasis placed on differences between the community tolerance level and the community tolerance spread for each site.

Utilizing explosives to destroy questionable munitions is a standard procedure in the military. It is critical to have teams trained to perform these activities safely. Often the training involves practice in setting up and detonating relatively small charges of explosives, but these activities can cause annoyance in surrounding civilian populations. A study was performed at one military installation to determine best practices for managing the noise generated by these activities. Three methods were investigated: burying charges with sandbags, covering charges with a rubber blast mat, and spraying water over the charges during detonation. Acoustic and seismic measurements were performed at several distances between 4 and 2500 m in two directions to investigate the relative effectiveness of each method. The study found that covering the charges with sandbags provided a reduction in noise levels of as much as 15 dB in the far field with minimal impact on the training. Use of sandbags was therefore superior ...

As a result of suburban sprawl, the number of people living near military installations is drastically increasing. Coupled with an escalation of military activities and preparedness, the potential for noise generated by an installation to impact the surrounding communities has grown, especially for large amplitude impulsive events such as those generated during artillery training exercises. To assess the effect of blast noise on individuals living near installations, a large scale in situ study has been performed. The homes of study participants were instrumented and outdoor/indoor blast signature pairs of routine installation activities were captured over the course of 1 year. Participants filled out short questionnaires whenever they heard blast noise events. Measurements of single events at subjects’ homes along with their responses present unique data with which to investigate the human response to blast noise on an event‐by‐event basis. In this presentation, the characteristics of the noise typically...