Wednesday (Group 2) - SERDP-ESTCP - Strategic Environmental ...

Sustainable Infrastructure (SI)
Facilities Management — Noise
Poster Number 76 – Wednesday
CHARACTERIZATION OF A BAYESIAN CLASSIFIER TO IDENTIFY MILITARY
T
IMPULSE NOISE
JEFFREY S. VIPPERMAN
University of Pittsburgh
531 Benedum Hall
3700 O’Hara Street
Pittsburgh, PA 15261
(412) 624-1643
jsv@pitt.edu
CO-PERFORMERS: Brian Bucci and Matthew Rhudy (University of Pittsburgh)
o facilitate a better relationship between military installation and their surrounding civilian
population, the function of military installations impulse noise monitoring stations needs to
be improved. The monitoring stations currently in service suffer numerous false positive
detections, (which are believed to originate from wind noise), and also do not detect many
significant impulse events. Most of the previous work within this effort has focused on defining
metrics to identify military impulse noise and processing these metrics with artificial neural
networks and Bayesian classifiers. More recent work has also been directed towards identifying
military impulse noise from direct temporal processing of the recorded waveforms. This previous
work, while successful, had focused largely on achieving maximal overall accuracy, without
much regard for the nature of the inevitable misclassifications (false positive detections and false
negative rejections). In an actual implementation of these algorithms, the nature of the
misclassifications would be of great concern to the user. To this end, this new effort focused on
characterizing the Bayesian classifier. The purpose of this project is to find the optimal number
of multi-Gaussian fits for each class of noise that would produce the classifiers with the
following characteristics: (1) maximal overall accuracy, (2) maximal accuracy with no false
positive detections, and (3) maximal accuracy with no false negative rejections. Additionally, it
is desired to determine the prior probabilities of each noise source that correspond to these
characteristics and the range of prior probabilities that produce classifiers which operate within
the optimal range (maximal accuracy with no false positive detections to maximal accuracy with
no false negative rejections). When the classifiers are evaluated on testing data, the maximal
accuracy is 99.8%, the maximal accuracy with no false negative rejections is also 99.8%, and the
maximal accuracy with no false positive detections is 98.4%.
This work is funded by SERDP Project SI-1585.
G-35