The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Dans cet article, nous proposons un système d'aide à la conception automatique pour les dispositifs acoustiques compacts tels que les microhaut-parleurs à l'intérieur des smartphones. Le système d'aide à la conception proposé produit les dimensions de dispositifs acoustiques compacts présentant la caractéristique acoustique souhaitée. Ce système utilise un réseau neuronal profond (DNN) pour obtenir la relation entre la caractéristique de fréquence du dispositif acoustique compact et ses dimensions. Les données d'entraînement sont générées par la méthode acoustique du domaine temporel à différences finies (FDTD) afin que de nombreuses données d'entraînement puissent être facilement obtenues. Nous démontrons l'efficacité du système proposé à travers quelques comparaisons entre les caractéristiques de fréquence souhaitées et conçues.
Kai NAKAMURA
Kansai University
Kenta IWAI
Ritsumeikan University
Yoshinobu KAJIKAWA
Kansai University
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Kai NAKAMURA, Kenta IWAI, Yoshinobu KAJIKAWA, "Acoustic Design Support System of Compact Enclosure for Smartphone Using Deep Neural Network" in IEICE TRANSACTIONS on Fundamentals,
vol. E102-A, no. 12, pp. 1932-1939, December 2019, doi: 10.1587/transfun.E102.A.1932.
Abstract: In this paper, we propose an automatic design support system for compact acoustic devices such as microspeakers inside smartphones. The proposed design support system outputs the dimensions of compact acoustic devices with the desired acoustic characteristic. This system uses a deep neural network (DNN) to obtain the relationship between the frequency characteristic of the compact acoustic device and its dimensions. The training data are generated by the acoustic finite-difference time-domain (FDTD) method so that many training data can be easily obtained. We demonstrate the effectiveness of the proposed system through some comparisons between desired and designed frequency characteristics.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/transfun.E102.A.1932/_p
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@ARTICLE{e102-a_12_1932,
author={Kai NAKAMURA, Kenta IWAI, Yoshinobu KAJIKAWA, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Acoustic Design Support System of Compact Enclosure for Smartphone Using Deep Neural Network},
year={2019},
volume={E102-A},
number={12},
pages={1932-1939},
abstract={In this paper, we propose an automatic design support system for compact acoustic devices such as microspeakers inside smartphones. The proposed design support system outputs the dimensions of compact acoustic devices with the desired acoustic characteristic. This system uses a deep neural network (DNN) to obtain the relationship between the frequency characteristic of the compact acoustic device and its dimensions. The training data are generated by the acoustic finite-difference time-domain (FDTD) method so that many training data can be easily obtained. We demonstrate the effectiveness of the proposed system through some comparisons between desired and designed frequency characteristics.},
keywords={},
doi={10.1587/transfun.E102.A.1932},
ISSN={1745-1337},
month={December},}
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TY - JOUR
TI - Acoustic Design Support System of Compact Enclosure for Smartphone Using Deep Neural Network
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 1932
EP - 1939
AU - Kai NAKAMURA
AU - Kenta IWAI
AU - Yoshinobu KAJIKAWA
PY - 2019
DO - 10.1587/transfun.E102.A.1932
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E102-A
IS - 12
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - December 2019
AB - In this paper, we propose an automatic design support system for compact acoustic devices such as microspeakers inside smartphones. The proposed design support system outputs the dimensions of compact acoustic devices with the desired acoustic characteristic. This system uses a deep neural network (DNN) to obtain the relationship between the frequency characteristic of the compact acoustic device and its dimensions. The training data are generated by the acoustic finite-difference time-domain (FDTD) method so that many training data can be easily obtained. We demonstrate the effectiveness of the proposed system through some comparisons between desired and designed frequency characteristics.
ER -