Blast noise classification with common sound level meter metrics.

@article{Cvengros2012BlastNC,
  title={Blast noise classification with common sound level meter metrics.},
  author={Robert M. Cvengros and Dan Valente and Edward T. Nykaza and Jeffrey S. Vipperman},
  journal={The Journal of the Acoustical Society of America},
  year={2012},
  volume={132 2},
  pages={
          822-31
        }
}
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… 

Figures and Tables from this paper

SIX NOISE TYPE MILITARY SOUND CLASSIFIER
Blast noise from military installations often has a negative impact on the quality of life of residents living in nearby communities. This, in turn, negatively impacts the military’s testing &
Objectively Choosing Spectrogram Parameters to Classify Environmental Noises
TLDR
It is found that the random sampling procedure is a useful way of choosing the spectrogram input-parameters, and finding the spectrograms features that are the most important for classifying environment noises.

References

SHOWING 1-10 OF 13 REFERENCES
Performance of artificial neural network-based classifiers to identify military impulse noise.
TLDR
In this project, classifiers based on artificial neural networks were developed to improve the accuracy of military impulse noise identification and achieved up to 100% accuracy on the training data and the validation data, while improving detection threshold by at least 40 dB.
An Investigation of the Characteristics of a Bayesian Military Impulse Noise Classifier
In extension of previous methods to identify military impulse noise in the civilian environmental noise monitoring setting by means of a set of computed scalar metrics input to artificial neural
Dimension Reduction in Text Classification with Support Vector Machines
TLDR
Novel dimension reduction methods to reduce the dimension of the document vectors dramatically are adopted and decision functions for the centroid-based classification algorithm and support vector classifiers are introduced to handle the classification problem where a document may belong to multiple classes.
A real-time blast noise detection and wind noise rejection system
Wind-induced noise causes many problems for unattended noise monitoring. This is especially true for the monitoring of low-frequency artillery blast noise. The wind noise can effectively mask the
Blast noise characteristics as a function of distance for temperate and desert climates.
TLDR
Variability in received sound levels were investigated at distances ranging from 4 m to 16 km from a typical blast source in two locations with different climates and terrain, and as expected, higher frequency energy is attenuated more drastically than the lower frequency energy as the distance from the source increases.
Support-Vector Networks
TLDR
High generalization ability of support-vector networks utilizing polynomial input transformations is demonstrated and the performance of the support- vector network is compared to various classical learning algorithms that all took part in a benchmark study of Optical Character Recognition.
Gene Selection for Cancer Classification using Support Vector Machines
TLDR
This paper proposes a new method of gene selection utilizing Support Vector Machine methods based on Recursive Feature Elimination (RFE), and demonstrates experimentally that the genes selected yield better classification performance and are biologically relevant to cancer.
“ SI - 1427 Final Report : Impulse Noise Bearing and Amplitude Measurement and Analsis 354 System ”
  • 2009
“ Dimension Reduction in Test Classification with SupportVector Ma - 365 chines ”
  • J . of Mach . Learn . Res .
  • 2005
...
1
2
...