A Wavelet Transform-based Feature Extraction Pipeline for Elephant Rumble Detection

Abstract

Elephants generate infrasonic vocalisations that traverse through the air for long distances. Utilising this phenomenon, a previous work proposed a system, called Eloc, to localise and track elephants in the wild. The Eloc system has been demonstrated to be accurate in calculating the location of infrasonic sources. However, it still lacks the capability to accurately distinguish elephant infrasonic calls from various other infrasonic sources using limited computing power on board. Addressing this problem, the work presented in this paper introduces an approach to distinguish elephant infrasonic calls with a high accuracy on low-resourced hardware. Firstly, a sequence of operations are performed to reduce the effect of noise in the infrasonic signal captured by an Eloc node. Secondly, a wavelet-based signal reconstruction technique is applied to extract spectral features from the infrasonic signal. Finally, the extracted features are fed to a pre-trained machine learning classifier to distinguish the infrasonic vocalisations of elephants. The experimental evaluation using Asian elephant (Elephas Maximus Maximus) infrasonic vocalisation datasets demonstrates that the proposed approach is capable of accurately distinguishing elephant infrasonic calls on low-resourced hardware platform of the Eloc system, with accuracy levels over 82% under varying environmental conditions.