Abstract:
The hazardous acoustic event classification
system is presented and tested in threatening
environments. The system is based on classified
with Support Vector Machine (SVM), k Nearest
Neighbor (kNN) and modeled with Genetic
Regulatory Network (GRN). GRN is adopted as
classification framework and greatly reduced
input feature dimensions. Setting the results that
have already reduced the inputs dimensions from
GRN framework as inputs for SVM and kNN can
correctly classify audio event with low
computational time and cost. Comparative and
classification tests are carried out using three
kinds of input sets with SVM and kNN classifier.
These input sets are original feature set, reduced
dimension feature set by GRN and unique feature
set. SVM applies as novel discriminative
approach for dissimilarity measure in order to
address a supervised sound-classification task
and then shows good performance in the task of
acoustic event classification. Selecting GRN in
event classification system can not only reduces
cost and effort but also aims to obtain high
performance and accuracy in varying nature of
environments.
