Visible to the public Multi-format speech perception hashing based on time-frequency parameter fusion of energy zero ratio and frequency band variance

TitleMulti-format speech perception hashing based on time-frequency parameter fusion of energy zero ratio and frequency band variance
Publication TypeConference Paper
Year of Publication2019
AuthorsHuang, Y., Wang, Y.
Conference Name2019 3rd International Conference on Electronic Information Technology and Computer Engineering (EITCE)
Date PublishedOct. 2019
PublisherIEEE
ISBN Number978-1-7281-3584-7
Keywordscryptography, energy zero ratio, feature extraction, frequency band variance, hash sequence security, key dependence, Libraries, mean filtering, message authentication, Metrics, Microsoft Windows, multiformat speech perception hashing, perceptual hash, pubcrawl, resilience, Resiliency, Robustness, Scalability, security, sensor fusion, speech content authentication, speech processing, Speech recognition, speech signal processing, Time Frequency Analysis, Time-frequency Analysis, time-frequency features, time-frequency parameter fusion
Abstract

In order to solve the problems of the existing speech content authentication algorithm, such as single format, ununiversal algorithm, low security, low accuracy of tamper detection and location in small-scale, a multi-format speech perception hashing based on time-frequency parameter fusion of energy zero ratio and frequency band bariance is proposed. Firstly, the algorithm preprocesses the processed speech signal and calculates the short-time logarithmic energy, zero-crossing rate and frequency band variance of each speech fragment. Then calculate the energy to zero ratio of each frame, perform time- frequency parameter fusion on time-frequency features by mean filtering, and the time-frequency parameters are constructed by difference hashing method. Finally, the hash sequence is scrambled with equal length by logistic chaotic map, so as to improve the security of the hash sequence in the transmission process. Experiments show that the proposed algorithm is robustness, discrimination and key dependent.

URLhttps://ieeexplore.ieee.org/document/9094822
DOI10.1109/EITCE47263.2019.9094822
Citation Keyhuang_multi-format_2019