Compression Algorithm of Voice Control Commands of Uav Based  on Wavelet Transform

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

doi:10.18372/1990-5548.55.12746

Cited by 3 publications

(2 citation statements)

References 0 publications

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“…Thus, we get blocks of normalized text information

that are ready for integration into a normalized audio signal

. However, since the integration does not take place in the audio signal

itself, but in its low-frequency wavelet coefficients (LFWC) followed by their scalar product with low-frequency (LPF-D) and high-frequency (HPF-D) orthogonal Daubechies wavelet filters at each

level of the wavelet decomposition, it is necessary to perform a wavelet transform of the audio signal

and find the low-frequency (LFWC) and high-frequency (HFWC) wavelet coefficients for each

level of the wavelet decomposition [ 30 , 31 ]. It should be noted that not only Daubechies filters can be used, but also other orthogonal wavelet filters, such as Coiflets, Symlets, or Meyer.…”

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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“…Thus, we get blocks of normalized text information

that are ready for integration into a normalized audio signal

. However, since the integration does not take place in the audio signal

itself, but in its low-frequency wavelet coefficients (LFWC) followed by their scalar product with low-frequency (LPF-D) and high-frequency (HPF-D) orthogonal Daubechies wavelet filters at each

level of the wavelet decomposition, it is necessary to perform a wavelet transform of the audio signal

and find the low-frequency (LFWC) and high-frequency (HFWC) wavelet coefficients for each

level of the wavelet decomposition [ 30 , 31 ]. It should be noted that not only Daubechies filters can be used, but also other orthogonal wavelet filters, such as Coiflets, Symlets, or Meyer.…”

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

show abstract

“…Багато фірм, що займаються побудовою систем захисту інформації публікують результати вимірювань загасання поля порівняно з розрахунковим значенням поля у вільному просторі. При цьому відмінність в інформації, що публікується, дуже велика [11,[12][13][14].…”

Section: електромагнітні хвилі всередині приміщенняunclassified

Peculiarities of Selection of the Electromagnetic Distribution Model Radiation Inside the Room

Бахтiяров

2019

SBT

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Система Распознавания Стеганографически-Преобразованных Голосовых Команд Управления Бпла

Лавриненко

Кочергин

Конахович

2018

reks

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It is created the system of recognition the steganographic-transformed voice commands of unmanned aerial vehicle control based on a cepstral analysis. It provides effective recognition and hidden commands transmission of to the board of an unmanned aerial vehicle, by converting voice control commands into a kind of steganographic characteristics vector, which implies the concealment of voice control information of an unmanned aerial vehicle. The mathematical model of the algorithm for calculating the mel-frequency cepstral coefficients and the recognition classifier of voice control commands for the solution of the problem of semantic identification and securing the control information of the unmanned aerial vehicle in the communication channel is synthesized. A software package has been developed that includes tools for compiling the base of reference voice images of subjects of management for training and testing the system for recognizing steganographic-transformed voice commands of the unmanned aerial vehicle control based on the cepstral analysis and computer models of the proposed methods and algorithms for recognition voice control commands in the MATLAB environment. The expediency of applying the proposed system for recognizing steganographic-transformed voice commands of the unmanned aerial vehicle control based on a cepstral analysis is substantiated and experimentally proved. An algorithm is presented for calculating the mel-frequency cepstral coefficients that appear in the role of the main features of recognition and the result of the steganographic transformation of speech, where for the evaluation of automatic recognition of voice commands using the results of classifier constructed by the criterion of minimum distance in the role which acts as the variance of the difference of the expectation of a mel-frequency cepstral coefficients. The obtained results of the experimental research allow to draw a conclusion about the expediency of further practical application of the developed system of recognition the steganographic-transformed voice commands of the unmanned aerial vehicle control based on the cepstral analysis

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Compression Algorithm of Voice Control Commands of Uav Based on Wavelet Transform

Cited by 3 publications

References 0 publications

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

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

Peculiarities of Selection of the Electromagnetic Distribution Model Radiation Inside the Room

Система Распознавания Стеганографически-Преобразованных Голосовых Команд Управления Бпла

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