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.

翻译：本文提出了一种识别地震次声信号中大象隆隆声的有效方法。介绍了放大、滤波和数字化通过地检波器采集的地震信号的电子电路设计与实现。在斯里兰卡一处大象孤儿院的自由活动区域收集了地震次声大象隆隆声数据集。将地震隆隆声转换为频谱图，并采用多种方法进行频谱特征提取。利用LasyPredict工具，将不同方法提取的特征输入对应的机器学习算法进行训练，以实现地震隆隆声的自动识别。研究发现，梅尔频率倒谱系数结合岭回归分类器机器学习算法在识别地震大象隆隆声方面表现最佳。此外，还提出了一种新颖的频谱去噪方法，可提高地震隆隆声识别的准确性。