A Hybrid Approach for Acoustic Signal Segmentation by Computing Similarity Matrix, Novelty Score and Peak Detection for Vehicular Classification in Wireless Sensor Networks
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Date
2010
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Abstract
Vehicle acoustic signals have long been considered as
significant source in sensor networks for classification. In this
research acoustic signals generated by each vehicle will be used to
detect its presence and classify the type. Vehicle acoustic signal
segmentation is important for continuous signal recognition
because it reduces the search space effectively in vehicle’s signal
recognition. However, for Vehicle classification, it is difficult to
segment the signal input reliably into useful sub-units because i)
vehicle sound units can often be located roughly via intensity
changes ii) energy changes in signal spectrum or amplitude help
to estimate unit boundaries, but these cues are often unreliable.
In this paper the series of steps proposed are signal segmentation
is presented, includes decomposition of signals into successive
frames of 50 ms without overlap. The computations of the
spectrum representation (FFT) of the frames are carried out. The
similarity matrix that shows the similarity between the spectriims
of different frames is computed. Estimation of the novelty score
related to the similarity matrix is done. The detection of the
peaks in the novelty score is made and finally segmenting the
vehicle acoustic signals using the peaks as position is done. These
segmented signals are further used for feature extraction and
'■'assification.