Blast noise from military installations often has a negative impact on the quality of life of residents living in nearby communities. This, in turn, negatively impacts the military’s testing & training capabilities due to restrictions, curfews, or range closures enacted to address noise complaints. In order to more directly manage noise around military installations, accurate noise monitoring around bases has become a necessity. Although most noise monitors are simple sound level meters, more recent ones are capable of discerning blasts from ambient noise with some success. Investigators at the University of Pittsburgh (Pitt) developed a more advanced noise classifier that can discern between wind, aircraft, and blast noise, while simultaneously lowering the measurement threshold. Here, more recent work between Pitt and the US Army Engineer Research and Development Center will be presented from the development of a more advanced classifier that identifies additional classes of noise such as machine gun fire, vehicles, and electronic noise. Additional signal metrics were explored given the increased complexity of the classifier. By broadening the types of noise the system can accurately classify and increasing the number of metrics, a new system was developed with increased blast noise accuracy, decreased number of missed events, and significantly fewer false positives.

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