## 7 Conclusions  
Electronic circuitry and signal conditioning techniques were developed quite successfully leading to the acquisition of seismic elephant rumbles with good quality. A few elephant rumbles were visually and audibly noticed during the data collection. Their seismic infrasonic copies were extracted from the geophone data, and the spectrograms were determined. The spectrogram enhancement reduced noise in the seismic data quite significantly leading to higher accuracy in feature detection and machine learning. Using LazyPredict, it was found that the MFCC feature extraction method and ridge classifier ML algorithm gave the best performance in automatic elephant rumble detection.  
The proposed elephant rumble identification method could be used to devise an early warning system for the villages troubled by wild elephants. The circuits involved are low-power and low-cost. It could be made even more cost-effective and less power-consuming by replacing the Raspberry Pi board with a Raspberry Pi zero board, which would extend the battery life in field deployment. For a practical deployment, the best ML algorithm, the ridge classifier, can be implemented on an edge device using the scikit-learn library in Python. It is important to mention that elephants do not rumble often; hence, rumble detection will not lead to the development of a perfect early warning system. Elephant localization using seismic rumbles has yet to be investigated, though some early work has been reported.  
Conceptualization, S.R.M.; methodology, J.V., C.S., R.H., U.G., C.U.S.E. and S.R.M.; validation, J.V., C.S., R.H., U.G., C.U.S.E. and S.R.M.; formal analysis, J.V., C.S., R.H., U.G., C.U.S.E. and S.R.M.; investigation, J.V., C.S., R.H., U.G., C.U.S.E. and S.R.M.; resources, J.V., C.S., R.H., U.G. and S.R.M.; data curation, J.V., U.G. and C.U.S.E.; writing--original draft preparation, J.V., C.S., R.H. and U.G.; writing--review and editing, C.U.S.E. and S.R.M.; supervision, C.U.S.E. and S.R.M.; funding acquisition, C.U.S.E. and S.R.M. All authors have read and agreed to the published version of the manuscript.  
The APC was funded in part by the Senate Research Committee, University of Moratuwa.  
Not applicable.  
The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author/s.  
The authors would like to thank [PERSON], Director of the Elephant Listening Project, [PERSON], Chair of the Earth and Atmospheric Sciences at Cornell University,  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Spectrogram** & **SSIM with the Initial Spectrum [8]** & **SSIM with the Enhanced Spectrum** \\ \hline Figure 9 (left) & 0.86 & 0.65 \\ \hline Figure 9 (right) & 0.89 & 0.66 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Denoising effect in the spectrogram enhancement process, measured with the SSIM index.  
USA, and [PERSON]. [PERSON], at Oklahoma City Zoo and Botanical Garden, USA for their insightful guidance. [PERSON], [PERSON], and [PERSON] are acknowledged for their facilitation and guidance provided for this research during the Fulbright Fellowship of the principal investigator [PERSON] at the Dept of Global Development, CALS, Cornell University, Ithaca, NY 14853, USA. The authors would also acknowledge the staff of the National Zoological Department, and the Elephant Orphanage, Pinnawala, Sri Lanka for their approval and invaluable support during data collection.  
The authors declare no conflicts of interest.