Date of Award
12-1-2005
Document Type
Thesis
Degree Name
M.S. in Engineering Science
First Advisor
William G. Frazier
Second Advisor
James P. Chambers
Third Advisor
Roger M. Waxler
Relational Format
Dissertation/Thesis
Abstract
Acoustic signature classification offers a unique capability of exploiting acoustic sensor data to computationally discriminate between detected sources. There are many variations of different techniques currently being used for acoustic classification. A comparison of some of these methods is presented and, performance analyses are documented for each test case within the matrix of presented techniques. This matrix consists of power spectral density and Prony type feature vectors combined with Bayesian maximum likelihood and backpropagation neural network classifiers. Combinations of each feature vector type and classifier type are tested with both simulated data and real data. The data represents short duration continuous sound of the various targets used for testing. Advantages, disadvantages, and recommendations are discussed for all the elements of each technique presented.
Recommended Citation
Williams, Jay E., "A study of acoustic signature classification methods and results" (2005). Electronic Theses and Dissertations. 3214.
https://egrove.olemiss.edu/etd/3214