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

doi:10.1109/ACCESS.2020.3036048

	A classification retrieval method for encrypted speech based on deep neural network and deep hashing
	Zhang, Qiuyu; Zhao, Xuejiao; Hu, Yingjie
刊名	IEEE Access
	2020
卷号	8 页码:202469-202482
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
ISSN号	2169-3536
DOI	10.1109/ACCESS.2020.3036048
英文摘要	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.
WOS研究方向	Computer Science ; Engineering ; Telecommunications
语种	英语
出版者	Institute of Electrical and Electronics Engineers Inc.
WOS记录号	WOS:000591199700001
内容类型	期刊论文
源URL	[http://ir.lut.edu.cn/handle/2XXMBERH/151268]
专题	计算机与通信学院
作者单位	School of Computer and Communication, Lanzhou University of Technology, Lanzhou; 730050, China
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.