Improving the Recognition Accuracy of a Sound Communication System Designed with a Neural Network
Summary :
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.
- Publication
- IEICE TRANSACTIONS on Fundamentals Vol.E104-A No.11 pp.1577-1584
- Publication Date
- 2021/11/01
- Publicized
- 2021/05/06
- Online ISSN
- 1745-1337
- DOI
- 10.1587/transfun.2020EAP1118
- Type of Manuscript
- PAPER
- Category
- Engineering Acoustics
Authors
Kosei OZEKI
Hokkaido University
Naofumi AOKI
Hokkaido University
Saki ANAZAWA
Hokkaido University
Yoshinori DOBASHI
Hokkaido University
Kenichi IKEDA
Smart Solution Technology, Inc.
Hiroshi YASUDA
Smart Solution Technology, Inc.
Keyword
Latest Issue
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Kosei OZEKI, Naofumi AOKI, Saki ANAZAWA, Yoshinori DOBASHI, Kenichi IKEDA, Hiroshi YASUDA, "Improving the Recognition Accuracy of a Sound Communication System Designed with a Neural Network" in IEICE TRANSACTIONS on Fundamentals,
vol. E104-A, no. 11, pp. 1577-1584, November 2021, doi: 10.1587/transfun.2020EAP1118.
Abstract: 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.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/transfun.2020EAP1118/_p
Copy
@ARTICLE{e104-a_11_1577,
author={Kosei OZEKI, Naofumi AOKI, Saki ANAZAWA, Yoshinori DOBASHI, Kenichi IKEDA, Hiroshi YASUDA, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Improving the Recognition Accuracy of a Sound Communication System Designed with a Neural Network},
year={2021},
volume={E104-A},
number={11},
pages={1577-1584},
abstract={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.},
keywords={},
doi={10.1587/transfun.2020EAP1118},
ISSN={1745-1337},
month={November},}
Copy
TY - JOUR
TI - Improving the Recognition Accuracy of a Sound Communication System Designed with a Neural Network
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 1577
EP - 1584
AU - Kosei OZEKI
AU - Naofumi AOKI
AU - Saki ANAZAWA
AU - Yoshinori DOBASHI
AU - Kenichi IKEDA
AU - Hiroshi YASUDA
PY - 2021
DO - 10.1587/transfun.2020EAP1118
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E104-A
IS - 11
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - November 2021
AB - 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.
ER -
English
