A digital speech signal compression algorithm based on wavelet transform

Konakhovych, G. F.; Lavrynenko, Oleksandr; Antonov, Vyacheslav; Bakhtiiarov, Denys

doi:10.18372/1990-5548.48.11204

Cited by 3 publications

(2 citation statements)

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“…Then, the discrete wavelet transform is the scalar product of the values of the studied audio signal

, with the coefficients of the orthogonal Daubechies wavelet filters of low

(LPF-D) and high

(HPF-D) frequencies for decomposition, followed by a double thinning

of the obtained coefficients

which can be formalized as follows:

where

, and

are low-frequency (LFWC) and high-frequency (HFWC) wavelet coefficients for the 1st level of audio signal

decomposition [ 32 , 33 ].…”

Section: Presentation Of the Proposed Methodsmentioning

confidence: 99%

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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“…Then, the discrete wavelet transform is the scalar product of the values of the studied audio signal

, with the coefficients of the orthogonal Daubechies wavelet filters of low

(LPF-D) and high

(HPF-D) frequencies for decomposition, followed by a double thinning

of the obtained coefficients

which can be formalized as follows:

where

, and

are low-frequency (LFWC) and high-frequency (HFWC) wavelet coefficients for the 1st level of audio signal

decomposition [ 32 , 33 ].…”

Section: Presentation Of the Proposed Methodsmentioning

confidence: 99%

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

Veselska

Lavrynenko

Одарченко

et al. 2022

Sensors

View full text Add to dashboard Cite

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“…Filter's edges are calculated in melscale. This scale is the result of research on the human ear's ability to perceive sounds at different frequencies [3].…”

Section: Theory and Methods Of Signal Processingmentioning

confidence: 99%

Voice Control Command Recognition System of Uav Based on Steganographic-Cepstral Analysis

Lavrynenko

Kocherhin

Konakhovych

2018

ECS

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The article suggests the developed voice command recognition system based on steganographic-cepstral analysis and the computational algorithm of mel-frequency cepstral coefficients which is used for creation the basic voice command recognition system on the basis of which can be created more complex voice control solutions. To prove experimentally and substantiate the usefulness of using the developed voice command recognition system and the computational algorithm of mel-frequency cepstral coefficients. The detailed results of preliminary experimental studies of the developed voice command recognition system and the computational algorithm of mel-frequency cepstral coefficients in MATLAB in the case of the identification of control commands "up", "down", "right", "left" spoken by different subjects. The article presents the developed voice command recognition system based on steganographic-cepstral analysis which allows to increase the efficiency of voice command recognition. The conclusion based on the detailed results of preliminary experimental studies shows the usefulness of using the developed voice command recognition system and the computational algorithm of mel-frequency cepstral coefficients and thorough substantiation of the scientific-technical experimental study.

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