Machine Learning Inspired Sound-Based Amateur Drone Detection for Public Safety Applications
Machine Learning Inspired Sound-Based Amateur Drone Detection for Public Safety Applications
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DOI：
10.1109/tvt.2019.2893615
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发表时间：
2019-03-01
影响因子：
6.8
通讯作者：
Jamalipour, Abbas
中科院分区：
文献类型：
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作者：
Anwar, Muhammad Zohaib;Kaleem, Zeeshan;Jamalipour, Abbas
近年来，无人驾驶飞行器由于其自主移动能力和在各个领域的应用而受到极大的欢迎。这也导致了一些严重的安全威胁，需要对业余无人机（ADr）进行适当的调查和及时检测，以保护安全敏感机构。在本文中，我们提出了一种新的机器学习（ML）框架，用于在嘈杂环境中检测和分类各种声音（如鸟，飞机和雷暴）中的ADr声音。为了从ADr声音中提取必要的特征，实现了Mel频率倒谱系数（MFCC）和线性预测倒谱系数（LPCC）特征提取技术。在特征提取之后，采用具有不同核函数的支持向量机（SVM）对这些声音进行准确分类。实验结果表明，支持向量机立方核与MFCC优于LPCC方法，达到约96.7%的准确率ADr检测。此外，结果验证了所提出的ML方案具有超过17%的检测精度，与基于相关性的无人机声音检测方案，忽略ML预测相比。
In recent years, popularity of unmanned air vehicles enormously increased due to their autonomous moving capability and applications in various domains. This also results in some serious security threats, that needs proper investigation and timely detection of the amateur drones (ADr) to protect the security sensitive institutions. In this paper, we propose the novel machine learning (ML) framework for detection and classification of ADr sounds out of the various sounds like bird, airplanes, and thunderstorm in the noisy environment. To extract the necessary features from ADr sound, Mel frequency cepstral coefficients (MFCC), and linear predictive cepstral coefficients (LPCC) feature extraction techniques are implemented. After feature extraction, support vector machines (SVM) with various kernels are adopted to accurately classify these sounds. The experimental results verify that SVM cubic kernel with MFCC outperform LPCC method by achieving around 96.7% accuracy for ADr detection. Moreover, the results verified that the proposed ML scheme has more than 17% detection accuracy, compared with correlation-based drone sound detection scheme that ignores ML prediction.