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|Title: ||A Sound Processing Pipeline for Robust Feature Extraction to Detect Elephant Rumbles|
|Authors: ||Silva, M. B. C. K.|
|Issue Date: ||2017|
A signi cant number of human and elephant lives have been lost due to the humanelephant
ict in Sri Lanka. To save lives of humans and elephants, it is therefore essential
to minimize encounters between them. An early warning system, which detects and localize
the presence of elephants through their infrasonic emissions is a viable solution to mitigate
icts. The high cost of infrasonic detectors is an important challenge to the realworld
deployment of such localization systems, in particular in developing countries where
the human-elephant con
ict occurs. ElOC is a system developed as a part of inventing a lowcost
automatic elephant detection and localization system. Which is capable of localizing
the infrasonic source within a ten-meter accuracy.
In this dissertation, a novel approach is proposed to extend the ELOC to identify the
elephant infrasound automatically. The novelty of this approach is the capability of distinguishing
the infrasonic emissions from the elephant on top of the low-cost, resource-limited
hardware platform of the ELOC. The approach rst applies a sequence of operations to
reduce the e ect of noise contained in the infrasonic signal captured by the ELOC node.
Then the spectral features of the infrasonic signal are extracted with wavelet-based signal
reconstruction to analyze the signal more precisely. Finally, the extracted features are feed
to the pre-trained classi er to distinguish the infrasound emissions from the elephants.
This study is able to classify elephant rumbles with an accuracy of 82%. Thereby the
proposed approach exhibits promising results in elephant detection and capable of operating
on the resource-limited hardware platform of the ELOC. This study also contributes to the
domain of digital signal processing since the study is the rst attempt of wavelet-based
feature extraction in the domain of infrasound elephant rumble detection.|
|Appears in Collections:||SCS Individual/Group Project - Final Thesis (2017)|
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