  This paper presents an effective method of identifying elephant rumbles in
infrasonic seismic signals. The design and implementation of electronic
circuitry to amplify, filter, and digitize the seismic signals captured through
geophones are presented. A collection of seismic infrasonic elephant rumbles
was collected at a free-ranging area of an elephant orphanage in Sri Lanka. The
seismic rumbles were converted to spectrograms, and several methods were used
for spectral feature extraction. Using LasyPredict, the features extracted
using different methods were fed into their corresponding machine-learning
algorithms to train them for automatic seismic rumble identification. It was
found that the Mel frequency cepstral coefficient (MFCC) together with the
Ridge classifier machine learning algorithm produced the best performance in
identifying seismic elephant rumbles. A novel method for denoising the spectrum
that leads to enhanced accuracy in identifying seismic rumbles is also
presented.
