计算机科学 ›› 2021, Vol. 48 ›› Issue (7): 225-232.doi: 10.11896/jsjkx.200500091
徐浩, 刘岳镭
XU Hao, LIU Yue-lei
摘要: 深度学习在图像识别和声音处理方面已经展现了它优越的性能和广阔的发展前景,对于在禁飞区设立的无人机侦测系统,使用深度学习的方法判断无人机的声音信号具有一定的意义。为了获得更优的侦测效果,首先列举了目前具有代表性的特征提取和分类方法,并分析其优缺点;然后提出了一种扩大可用样本数量的数据处理方式,同时在实验中使用不同组合的深度学习网络训练样本;最后通过混淆矩阵法,针对不同信噪比模型、滤波下限、拟合程度、神经网络组合和跨型号识别的实验效果进行评价。实验结果表明,适当地降低训练样本中的无人机声强可以增大系统的识别距离;使用MFCC提取声音特征,通过全连神经网络进行分类的样本识别的半径更远,误判率更低。
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[1]DARIO F,ROBERT J.Science,Technology and the Future of Small Autonomous Drones[J].Nature,2015,521(7553):460-466. [2]HASSANALIAN M,ABDELKEFI A.Classifications,Applications,and Design Challenges of Drones:A Review[J].Progress in Aerospace Sciences,2017,91:99-131. [3]COLOMINA I,MOLINA P.Unmanned Aerial Systems for Photogrammetry and Remote Sensing:A Review[J].Isprs Journal of Photogrammetry and Remote Sensing,2014,92:79-97. [4]MILAN E,ENRICO N,KAUSHIK R,et al.Help from the Sky:Leveraging UAVs for Disaster Management[J].IEEE Pervasive Computing,2017,16(1):24-32. [5]HUTTUNEN M.Civil Unmanned Aircraft Systems and Security:The European Approach[J].Journal of Transportation Security,2019,12(3):83-101. [6]SHI X F,YANG C Q,XIE W G,et al.Anti-drone system with multiple surveillance technologies:Architecture,implementation,and challenges[J].IEEE Communications Magazine,2018,9(1):68-74. [7]LI Q Z,XIONG R R,WANG R P,et al.Research on real-time UAV recognition method based on SSD Algorithm[J].Ship Electronic Engineering,2019,39(5):30-35. [8]ANDREA B,FEDERICA M,EMILIANO P,et al.Drone detection by acoustic signature identification[J].Electronic Imaging,2017,10(1):60-64. [9]HUANG G B,ZHOU H M,DING X J,et al.Exterme Learning Machine for Regression and Multiclass Classification[J].System Man and Cybernetics,2012,42(2):513-529. [10]KAROL J P.Environmental Sound Classification with Convolutional Neural Networks[C]//2015 IEEE 25th International Workshop on Machine Learning for Signal Processing(MLSP).2015:1-6. [11]KHAN S A,ANIL S T,JAGANNATH H N,et al.A Unique Approach in Text Independent Speaker Recognition Using MFCC Feature Sets and Probabilistic Neural Network[C]//2015 EighthInternational Conference on Advances in Pattern Recognition(ICAPR).2015:1-6. [12]WANG R P,FANG Y,CHEN D L,et al.Vehicle abnormalsound Recognition based on wavelet packet FBank Spectrogram and CNN[J].Journal of Congqing Uinversity of Technology(Natural Science),2020,34(7):1-9. [13]HARSHITA G,DIVYA G.LPC and LPCC Method of Feature Extracion in Speech Recognition System[C]//2015 Internatio-nal Conference-Cloud System and Big Data Engineering.2015:498-502. [14]MUHAMMAD Z A,ZEESHAN K,ABBAS J.Machine Lear-ning Inspired Sound-Based Amateur Drone Detection for Public Safety Applications[J].IEEE Transactions on Vehicular Technology,2019,68(3):2526-2534. [15]SAHIDULLAH M,GOUTAM S.Design,Analysis and Experimental Evaluation of Block Based Transformation in MFCC Computation for Speaker Recognition[J].Speech Communication,2012,54(4):543-565. [16]CHEN T,AO M Y,CHEN H Z.Study of speech recognitiontechnology based on MFCC and SVM[J].Journal of Guangxi Vocational and Technical College,2010,3(5):1-4. [17]WASEEM R,WANG Z H.Deep Convolutional Neural Net-works for Image Classification:A Comprehensive Review[J].Neural Computation,2017,29(9):2352-2449. [18]MATTHIAS H,MAKSYM A,JULIAN B.Why ReLU Net-works Yield High-Confidence Predictions Far Away From the Training Data and How to Mitigate the Problem[C]//2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition(CVPR).2019:41-50. [19]DARCH J,MILNER B,VASEGHI S.MAP prediction of formant frequencies and voicing class from MFCC vectors in noise[J].Speech Communication,2006,6(48):1556-1572. [20]HE K M,SUN J.Convolutional neural networks at constrained time cost[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.New York:IEEE Press,2015:5353-5360. [21]DENG X Y,LIU Q,DENG Y,et al.An Improved Method to Construct Basic Probability Assignment Based on the Confusion Matrix for Classification Problem[J].Information Sciences,2016,340:250-261. [22]SIRANI M P,LIU J H.Complexity Reduction,Self/Complete Recursive,Radix-2 DCT I/IV Algorithms[J].Journal of Computational and Applied Mathematics,2020,379:1-16. [23]GU S S,DING L,YANG Y,et al.A New Deep Learning MethodBased on AlexNet Model and SSD Model for Tennis Ball Recognition[C]//2017 IEEE 10th International Workshop on Computational Intelligence and Applications (IWCIA).2017:159-164. |
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