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Blast noise classification with common sound level meter metrics

Cvengros, RM and Valente, D and Nykaza, ET and Vipperman, JS (2012) Blast noise classification with common sound level meter metrics. Journal of the Acoustical Society of America, 132 (2). 822 - 831. ISSN 0001-4966

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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. © 2012 Acoustical Society of America.


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Item Type: Article
Status: Published
CreatorsEmailPitt UsernameORCID
Cvengros, RM
Valente, D
Nykaza, ET
Vipperman, JSjsv@pitt.eduJSV0000-0001-5585-954X
Date: 1 August 2012
Date Type: Publication
Journal or Publication Title: Journal of the Acoustical Society of America
Volume: 132
Number: 2
Page Range: 822 - 831
DOI or Unique Handle: 10.1121/1.4730921
Schools and Programs: Swanson School of Engineering > Mechanical Engineering and Materials Science
Refereed: Yes
ISSN: 0001-4966
Date Deposited: 21 Sep 2018 19:17
Last Modified: 02 Feb 2019 15:55


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