Neural Network Approaches and MSPCA in Vehicle Acoustic Signal Classification using Wireless Sensor Networks
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Date
2010
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Abstract
Acoustic communication has been widely used in
wireless sensor networks. Vehicle acoustic signals have long
been considered as unwanted traffic noise. In this research
acoustic signals generated by each vehicle will be used to
detect its presence and classify the type. The goal of multiscale
: PCA (MSPCA) is to reconstruct a simplified multivariate
signal, starting from a multivariate signal and using a simple
representation at each resolution level. Multiscale principal
components analysis generalizes the PCA of a multivariate
signal represented as a matrix by simultaneously performing
a PCA on the matrices of details at different levels. By
selecting the numbers of retained principal components,
simplified signals can be reconstructed. These simplified
signals are used for extracting the features. Six different
features of the vehicle acoustic signals are calculated for the
pre-processed acoustic vehicle signals and then further
utilized as input to the classification system. These features
include Signal Energy, Energy Entropy, Zero-Crossing Rate,
Spectral Roll-Off, Spectral Centroid and Spectral Flux.
Acoustic signal classification consists of extracting the
features from a sound, and of using these features to identify
classes the sound is liable to fit. Neural network approaches
used here are KNN, PNN and BPN and these three
approaches are combined with the MSPCA to obtain better
accuracy.