Improving the Recognition Accuracy of a Sound Communication System Designed with a Neural Network
Kosei Ozeki, Naofumi Aoki, Saki Anazawa, Yoshinori Dobashi, Kenichi Ikeda, Hiroshi Yasuda · IEICE Transactions on Fundamentals of Electronics Communications and Computer Sciences · 2021
This study has developed a system that performs data communications using high frequency bands of sound signals. Unlike radio communication systems using advanced wireless devices, it only requires the legacy devices such as microphones and speakers employed in ordinary telephony communication systems. In this study, we have investigated the possibility of a machine learning approach to improve the recognition accuracy identifying binary symbols exchanged through sound media. This paper describes some experimental results evaluating the performance of our proposed technique employing a neural network as its classifier of binary symbols. The experimental results indicate that the proposed technique may have a certain appropriateness for designing an optimal classifier for the symbol identification task.