IEICE TRANSACTIONS on Information
Broadband Active Noise Control Using a Neural Network
Summary :
This paper presents a neural network-based control system for Adaptive Noise Control (ANC). The control system derives a secondary signal to destructively interfere with the original noise to cut down the noise power. This paper begins with an introduction to feedback ANC systems and then describes our adaptive algorithm in detail. Three types of noise signals, recorded in destroyer, F16 airplane and MR imaging room respectively, were then applied to our noise control system which was implemented by software. We obtained an average noise power attenuation of about 20 dB. It was shown that our system performed as well as traditional DSP controllers for narrow-band noise and achieved better results for nonlinear broadband noise problems. In this paper we also present a hardware implementation method for the proposed algorithm. This hardware architecture allows fast and efficient field training in new environments and makes real-time real-life applications possible.
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- IEICE TRANSACTIONS on Information Vol.E81-D No.8 pp.855-861
- Publication Date
- 1998/08/25
- Publicized
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- Speech Processing and Acoustics
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Casper K. CHEN, Tzi-Dar CHIUEH, Jyh-Horng CHEN, "Broadband Active Noise Control Using a Neural Network" in IEICE TRANSACTIONS on Information,
vol. E81-D, no. 8, pp. 855-861, August 1998, doi: .
Abstract: This paper presents a neural network-based control system for Adaptive Noise Control (ANC). The control system derives a secondary signal to destructively interfere with the original noise to cut down the noise power. This paper begins with an introduction to feedback ANC systems and then describes our adaptive algorithm in detail. Three types of noise signals, recorded in destroyer, F16 airplane and MR imaging room respectively, were then applied to our noise control system which was implemented by software. We obtained an average noise power attenuation of about 20 dB. It was shown that our system performed as well as traditional DSP controllers for narrow-band noise and achieved better results for nonlinear broadband noise problems. In this paper we also present a hardware implementation method for the proposed algorithm. This hardware architecture allows fast and efficient field training in new environments and makes real-time real-life applications possible.
URL: https://global.ieice.org/en_transactions/information/10.1587/e81-d_8_855/_p
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@ARTICLE{e81-d_8_855,
author={Casper K. CHEN, Tzi-Dar CHIUEH, Jyh-Horng CHEN, },
journal={IEICE TRANSACTIONS on Information},
title={Broadband Active Noise Control Using a Neural Network},
year={1998},
volume={E81-D},
number={8},
pages={855-861},
abstract={This paper presents a neural network-based control system for Adaptive Noise Control (ANC). The control system derives a secondary signal to destructively interfere with the original noise to cut down the noise power. This paper begins with an introduction to feedback ANC systems and then describes our adaptive algorithm in detail. Three types of noise signals, recorded in destroyer, F16 airplane and MR imaging room respectively, were then applied to our noise control system which was implemented by software. We obtained an average noise power attenuation of about 20 dB. It was shown that our system performed as well as traditional DSP controllers for narrow-band noise and achieved better results for nonlinear broadband noise problems. In this paper we also present a hardware implementation method for the proposed algorithm. This hardware architecture allows fast and efficient field training in new environments and makes real-time real-life applications possible.},
keywords={},
doi={},
ISSN={},
month={August},}
Copy
TY - JOUR
TI - Broadband Active Noise Control Using a Neural Network
T2 - IEICE TRANSACTIONS on Information
SP - 855
EP - 861
AU - Casper K. CHEN
AU - Tzi-Dar CHIUEH
AU - Jyh-Horng CHEN
PY - 1998
DO -
JO - IEICE TRANSACTIONS on Information
SN -
VL - E81-D
IS - 8
JA - IEICE TRANSACTIONS on Information
Y1 - August 1998
AB - This paper presents a neural network-based control system for Adaptive Noise Control (ANC). The control system derives a secondary signal to destructively interfere with the original noise to cut down the noise power. This paper begins with an introduction to feedback ANC systems and then describes our adaptive algorithm in detail. Three types of noise signals, recorded in destroyer, F16 airplane and MR imaging room respectively, were then applied to our noise control system which was implemented by software. We obtained an average noise power attenuation of about 20 dB. It was shown that our system performed as well as traditional DSP controllers for narrow-band noise and achieved better results for nonlinear broadband noise problems. In this paper we also present a hardware implementation method for the proposed algorithm. This hardware architecture allows fast and efficient field training in new environments and makes real-time real-life applications possible.
ER -
English
