Acoustic Design Support System of Compact Enclosure for Smartphone Using Deep Neural Network

Kai NAKAMURA  Kenta IWAI  Yoshinobu KAJIKAWA  

Publication
IEICE TRANSACTIONS on Fundamentals of Electronics, Communications and Computer Sciences   Vol.E102-A   No.12   pp.1932-1939
Publication Date: 2019/12/01
Online ISSN: 1745-1337
DOI: 10.1587/transfun.E102.A.1932
Type of Manuscript: PAPER
Category: Engineering Acoustics
Keyword: 
design method for compact acoustic devices,  deep neural network (DNN),  finite-difference time-domain (FDTD) method,  

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Summary: 
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.