Development of an Inaudible Sound Communications System Based on a Machine Learning Approach
| Autor: | Kosei Ozeki, Naofumi Aoki, Yoshinori Dobashi |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
0209 industrial biotechnology
Artificial neural network Computer science business.industry 02 engineering and technology Communications system Machine learning computer.software_genre 020901 industrial engineering & automation Signal-to-noise ratio 0202 electrical engineering electronic engineering information engineering Spectrogram Wireless 020201 artificial intelligence & image processing Artificial intelligence Telephony business Classifier (UML) computer |
| Zdroj: | ICAIIC |
| DOI: | 10.1109/icaiic48513.2020.9065084 |
| Popis: | This study has developed a system that performs data communications using high frequency inaudible band of sound signals. Unlike radio communications using particular wireless devices, it only requires microphones and speakers employed in ordinary telephony communications. In this paper, we describe the possibility of a machine learning approach to identify the information embedded in sound signals. Our proposed technique identifies the symbols by reading sound spectrogram based on an image recognition approach. This paper describes some experimental results evaluating the performance of the proposed technique. It indicates that the proposed technique may have a certain appropriateness to design a classifier for the symbol identification in the inaudible sound communications. |
| Databáze: | OpenAIRE |
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