Publication:
Using a Neural Network to Convert a Radio Signal from an RTL-SDR Receiver to Text

Дата
2020
Авторы
Vildyaeva, M. V.
Egorova, E. A.
Vavrenyuk, A. B.
Journal Title
Journal ISSN
Volume Title
Издатель
Научные группы
Организационные подразделения
Организационная единица
Институт интеллектуальных кибернетических систем
Цель ИИКС и стратегия развития - это подготовка кадров, способных противостоять современным угрозам и вызовам, обладающих знаниями и компетенциями в области кибернетики, информационной и финансовой безопасности для решения задач разработки базового программного обеспечения, повышения защищенности критически важных информационных систем и противодействия отмыванию денег, полученных преступным путем, и финансированию терроризма.
Выпуск журнала
Аннотация
© 2020 IEEE.Voice recognition technology is now becoming increasingly popular. By translating an oral speech into text, people receive information for study, presented in a convenient format for working with it. This article deals in detail with interaction with RTL-SDR to obtain a digital signal with recording radio conversations and further converting them into a text document. The aim of the study is to find and implement a method that effectively solves the above-mentioned tasks. The problems associated with a large amount of audio information, which takes up a lot of memory resources for processing, are analyzed. The use of a neural network as a means of recognizing audio information is considered: this method provides the optimal way to obtain a finished text document while minimizing the steps of work and its execution time. The results obtained are analysed and conclusions are drawn on the further development of the study.
Описание
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Цитирование
Vildyaeva, M. V. Using a Neural Network to Convert a Radio Signal from an RTL-SDR Receiver to Text / Vildyaeva, M.V., Egorova, E.A., Vavrenyuk, A.B. // Proceedings of the 2020 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering, EIConRus 2020. - 2020. - P. 1449-1451. - 10.1109/EIConRus49466.2020.9039046
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