# Event Detection and Classification for Long Range Sensing of Elephants Using Seismic Signal

arXiv: https://arxiv.org/abs/2509.02920 · HTML: https://mlsanity.com/paper/2509.02920/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 80 references that are in the map.
- First posted on arXiv 3 Sep 2025.

## Builds on (most cited first, 6 of 6)

- [An overview of gradient descent optimization algorithms](https://mlsanity.com/paper/1609.04747/) (2016)
- [The Real-World-Weight Cross-Entropy Loss Function: Modeling the Costs of Mislabeling](https://mlsanity.com/paper/2001.00570/) (2020)
- [Automatic Detection and Compression for Passive Acoustic Monitoring of the African Forest Elephant](https://mlsanity.com/paper/1902.09069/) (2019)
- [Learning to rumble: Automated elephant call classification, detection and endpointing using deep architectures](https://mlsanity.com/paper/2410.12082/) (2024)
- [Detection of Seismic Infrasonic Elephant Rumbles Using Spectrogram-Based Machine Learning](https://mlsanity.com/paper/2312.02831/) (2023)
- [Towards Long Range Detection of Elephants Using Seismic Signals; A Geophone-Sensor Interface for Embedded Systems](https://mlsanity.com/paper/2406.05140/) (2024)

## Abstract (arXiv)

Detecting elephants through seismic signals is an emerging research topic aimed at developing solutions for Human-Elephant Conflict (HEC). Despite the promising results, such solutions heavily rely on manual classification of elephant footfalls, which limits their applicability for real-time classification in natural settings. To address this limitation and build on our previous work, this study introduces a classification framework targeting resource-constrained implementations, prioritizing both accuracy and computational efficiency. As part of this framework, a novel event detection technique named Contextually Customized Windowing (CCW), tailored specifically for detecting elephant footfalls, was introduced, and evaluations were conducted by comparing it with the Short-Term Average/Long-Term Average (STA/LTA) method. The yielded results show that the maximum validated detection range was 155.6 m in controlled conditions and 140 m in natural environments. Elephant footfall classification using Support Vector Machine (SVM) with a Radial Basis Function (RBF) kernel demonstrated superior performance across multiple settings, achieving an accuracy of 99% in controlled environments, 73% in natural elephant habitats, and 70% in HEC-prone human habitats, the most challenging scenario. Furthermore, feature impact analysis using explainable AI identified the number of Zero Crossings and Dynamic Time Warping (DTW) Alignment Cost as the most influential factors in all experiments, while Predominant Frequency exhibited significant influence in controlled settings.