Acoustic identification of buried underwater unexploded ordnance using a numerically trained classifier (L)

2012 ◽  
Vol 132 (6) ◽  
pp. 3614-3617 ◽  
Author(s):  
Joseph A. Bucaro ◽  
Zachary J. Waters ◽  
Brian H. Houston ◽  
Harry J. Simpson ◽  
Angie Sarkissian ◽  
...  
Keyword(s):  
Unexploded Ordnance ◽  
Trained Classifier ◽  
Acoustic Identification

Injuries from Landmines and Unexploded Ordnance in Afghanistan

2002 ◽  
Vol 17 (S2) ◽  
pp. S36
Author(s):  
Oleg O. Bilukha ◽  
M. Brennan ◽  
B. Woodruff
Keyword(s):  
Unexploded Ordnance

Squeak and rattle noise classification using radial basis function neural networks

2020 ◽  
Vol 68 (4) ◽  
pp. 283-293
Author(s):  
Oleksandr Pogorilyi ◽  
Mohammad Fard ◽  
John Davy ◽  
Mechanical and Automotive Engineering, School ◽  
Mechanical and Automotive Engineering, School ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
High Accuracy ◽  
Training Method ◽  
Vehicle Interior ◽  
Trained Classifier ◽  
Different Types ◽  
Noise Classification ◽  
Automatic Tool ◽  
Multi Class Classification

In this article, an artificial neural network is proposed to classify short audio sequences of squeak and rattle (S&R) noises. The aim of the classification is to see how accurately the trained classifier can recognize different types of S&R sounds. Having a high accuracy model that can recognize audible S&R noises could help to build an automatic tool able to identify unpleasant vehicle interior sounds in a matter of seconds from a short audio recording of the sounds. In this article, the training method of the classifier is proposed, and the results show that the trained model can identify various classes of S&R noises: simple (binary clas- sification) and complex ones (multi class classification).

Matched Filter Processor for Detection and Discrimination of Unexploded Ordnance

2003 ◽  
Author(s):  
Jeffrey Marqusee ◽  
George Robitaille ◽  
Thomas Bell
Keyword(s):  
Matched Filter ◽  
Unexploded Ordnance
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