Method of Semantic Coding of Speech Signals based on Empirical Wavelet Transform

Lavrynenko, Oleksandr; Одарченко, Роман; Konakhovych, G. F.; Taranenko, A. G.; Bakhtiiarov, Denys; Dyka, Tatiana

doi:10.1109/aict52120.2021.9628985

Cited by 2 publications

(1 citation statement)

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“…The correctness of this expression is confirmed by the fulfillment of the condition

where

is a number of samples of the audio signal,

is a number of coefficients of the Daubechies wavelet filter, and the symbol

characterizes the rounding down of a number

[ 27 , 28 , 29 ].…”

Section: Presentation Of the Proposed Methodsmentioning

confidence: 88%

A Wavelet-Based Steganographic Method for Text Hiding in an Audio Signal

Veselska

Lavrynenko

Одарченко

et al. 2022

Sensors

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The developed method of steganographic hiding of text information in an audio signal based on the wavelet transform acquires a deep meaning in the conditions of the use by an attacker of deliberate unauthorized manipulations with a steganocoded audio signal to distort the text information embedded in it. Thus, increasing the robustness of the stego-system by compressing the steganocoded audio signal subject to the preservation of the integrity of text information, taking into account the features of the psychophysiological model of sound perception, is the main objective of this scientific research. The task of this scientific research is effectively solved using a multilevel discrete wavelet transform using adaptive block normalization of text information with subsequent recursive embedding in the low-frequency component of the audio signal and further scalar product of the obtained coefficients with the Daubechies wavelet filters. The results of the obtained experimental studies confirm the hypothesis, namely that it is proposed to use recursive embedding in the low-frequency component (approximating wavelet coefficients) followed by their scalar product with wavelet filters at each level of the wavelet decomposition, which will increase the average power of hidden data. It should be noted that upon analyzing the existing method, which is based on embedding text information in the high-frequency component (detailed wavelet coefficients), at the last level of the wavelet decomposition, we obtained the limit CR = 6, and in the developed, CR = 20, with full integrity of the text information in both cases. Therefore, the resistance of the stego-system is increased by 3.3 times to deliberate or passive compression of the audio signal in order to distort the embedded text information.

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