A classification retrieval method for encrypted speech based on deep neural network and deep hashing

doi:10.1109/ACCESS.2020.3036048

IR > 计算机与通信学院

	A classification retrieval method for encrypted speech based on deep neural network and deep hashing
	Zhang, Qiuyu; Zhao, Xuejiao; Hu, Yingjie
	2020
发表期刊	IEEE Access
ISSN	2169-3536
卷号	8 页码:202469-202482
摘要	In order to improve the retrieval efficiency and accuracy of the existing encrypted speech retrieval methods, and improve the semantic representation of speech features and classification performance, a classification retrieval method for encrypted speech based on deep neural network (DNN) and deep hashing is proposed. Firstly, the speech files are classified according to the category tags, and the speech files are encrypted by Rossler chaotic map method and uploaded to the cloud encrypted speech library. Secondly, the Log-Mel spectrogram features of the original speech are extracted, and extract deep semantic features and generate classification results through the trained convolutional neural network (CNN) and convolutional recurrent neural network (CRNN). Finally, the semantic feature hash code is obtained through the constructed hash function, combined with the category hash code encoded by One Hot coding to obtain the final deep hashing binary code, and uploaded to the deep hashing index table. When retrieval, the deep hashing binary code of the query speech is obtained, and the ‘‘two-stage’’ classification retrieval strategy and the normalized Hamming distance algorithm are used to match the semantic feature hash. Experimental results show that the proposed two DNN coding models have excellent feature learning performance, and has better recall rate, precision rate and retrieval efficiency. © 2020 Institute of Electrical and Electronics Engineers Inc.. All rights reserved.
关键词	Binary codes Chaotic systems Classification (of information) Convolution Convolutional neural networks Deep neural networks Efficiency Hamming distance Hash functions Information retrieval Learning systems Semantics Speech
DOI	10.1109/ACCESS.2020.3036048
收录类别	EI ; SCIE
语种	英语
WOS研究方向	Computer Science ; Engineering ; Telecommunications
WOS类目	Computer Science, Information Systems ; Engineering, Electrical & Electronic ; Telecommunications
WOS记录号	WOS:000591199700001
出版者	Institute of Electrical and Electronics Engineers Inc.
EI入藏号	20211210118935
EI主题词	Recurrent neural networks
EI分类号	716.1 Information Theory and Signal Processing ; 723.1 Computer Programming ; 751.5 Speech ; 903.3 Information Retrieval and Use ; 913.1 Production Engineering ; 961 Systems Science
来源库	WOS
引用统计	被引频次：2[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	https://ir.lut.edu.cn/handle/2XXMBERH/151268
专题	计算机与通信学院
通讯作者	Zhang, Qiuyu
作者单位	Lanzhou Univ Technol, Sch Comp & Commun, Lanzhou 730050, Peoples R China
第一作者单位	兰州理工大学
通讯作者单位	兰州理工大学
第一作者的第一单位	兰州理工大学
推荐引用方式 GB/T 7714	Zhang, Qiuyu,Zhao, Xuejiao,Hu, Yingjie. A classification retrieval method for encrypted speech based on deep neural network and deep hashing[J]. IEEE Access,2020,8:202469-202482.
APA	Zhang, Qiuyu,Zhao, Xuejiao,&Hu, Yingjie.(2020).A classification retrieval method for encrypted speech based on deep neural network and deep hashing.IEEE Access,8,202469-202482.
MLA	Zhang, Qiuyu,et al."A classification retrieval method for encrypted speech based on deep neural network and deep hashing".IEEE Access 8(2020):202469-202482.

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