An encrypted speech retrieval algorithm based on Chirp-Z transform and perceptual hashing second feature extraction

Abstract

In order to satisfy the requirements of retrieval time-efficiency and security for encrypted speech data retrieval in the cloud environment, and to improve the impact of noise on the robustness and discrimination for the speech perceptual hashing scheme, an encrypted speech retrieval algorithm based on Chirp-Z transform and perceptual hashing second feature extraction is proposed in this paper. The speech owner first processes the original speech file by pre-processing, framing, and adding window. The features of the original speech file is extracted by Chirp-Z transform combined with the sparse random matrix to construct a hash sequence. Then encrypt the original speech file based on the m sequence and upload it to the cloud to ensure the security of information. By processing the speech perceptual hashing feature, the speech features are re-extracted, and the speech is evenly classified by k-means clustering technique. The binary string of several hundred bits is converted into a decimal number. Finally, the second feature is stored in system hash index table of the cloud. When the user retrieves, the query speech is denoised and the hash sequence is extracted. Then the secondary features of the hash sequence are extracted and matched with the encrypted speech features in the cloud system hash index table to obtain the retrieval result. The experimental results show that the proposed algorithm greatly compresses the information capacity of speech features, significantly improves the retrieval time-efficiency, with strong robustness and discrimination, and has a good retrieval effect on noisy speech.