An image processing based neural network method of wave form classification.
Jeffrey S. Vipperman, Brian A. Bucci · The Journal of the Acoustical Society of America · 2008
In an effort to identify military impulse noise, as it relates to civilian damage and disturbance claims, several metric based approaches utilizing artificial neural networks and Bayesian classifiers have proven successful in addressing this issue. However, in the course of research, it became apparent that the noise sources to be classified, namely, various types of military impulse noise, wind noise, and aircraft noise, could be easily identified by a minimally trained observer by way of a simple visual inspection of the wave form. Additionally, since the noise classification algorithm is desired to be implemented on DSP boards with possibly limited computational resources, it may prove beneficial to avoid the computation of metrics which involve complex mathematical operations. Borrowing from proven artificial neural network techniques already proven in the field of optical character recognition, this proposed noise classifier views a captured wave form as a number of points located on a spatial grid. The density of points within each grid sector is then used as input to an artificial neural network. The resulting classifiers performed with accuracies up to 0.997 on testing data. [This research was supported by the U.S. Department of Defense, through the Strategic Environmental Research and Development Program (SERDP).]