Document Detail

Blast noise classification with common sound level meter metrics.
MedLine Citation:
PMID:  22894205     Owner:  NLM     Status:  In-Data-Review    
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.
Robert M Cvengros; Dan Valente; Edward T Nykaza; Jeffrey S Vipperman
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Publication Detail:
Type:  Journal Article    
Journal Detail:
Title:  The Journal of the Acoustical Society of America     Volume:  132     ISSN:  1520-8524     ISO Abbreviation:  J. Acoust. Soc. Am.     Publication Date:  2012 Aug 
Date Detail:
Created Date:  2012-08-16     Completed Date:  -     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  7503051     Medline TA:  J Acoust Soc Am     Country:  United States    
Other Details:
Languages:  eng     Pagination:  822-31     Citation Subset:  IM    
U.S. Army Corps of Engineers, Engineer Research & Development Center, Champaign, Illinos 61820.
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