基于形成峰分析和隐马尔可夫模型的大象次声呼叫自动识别

J. Wijayakulasooriya
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引用次数: 25

摘要

提出了一种大象次声呼叫的自动识别方法。该算法采用线性预测编码(LPC)提取大象隆隆声的共振峰。提出了一种新的特征提取技术来捕捉大象隆隆声的独特特征。然后使用双状态隐马尔可夫模型(HMM)对特征进行处理,以自动检测大象的存在。该方法在纯象声和包含环境噪声的现场记录信号上进行了测试。结果表明,即使在噪声条件下,该方法也能成功地检测到大象的存在。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Automatic recognition of elephant infrasound calls using formant analysis and Hidden Markov Model
A method is proposed for automatic recognition of elephant infrasound calls. It uses Linear Predictive Coding (LPC) for extracting the formants of the elephant rumble. A novel feature extraction technique is proposed to capture the unique features of elephant rumbles. The features are then processed using a two state Hidden Markov Model (HMM) to automatically detect the presence of elephants. The method is tested on pure elephant rumbles as well as field recorded signals containing environmental noise. The results show that the proposed method can be used to successfully detect the presence of elephants even under noisy conditions.
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