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".
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The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Nous proposons une modélisation du chemin de rétroaction en ligne avec un système de contrôle actif du bruit de type pré-inverse (PIANC) pour suivre la fluctuation de manière stable dans le chemin de rétroaction. Le système conventionnel de contrôle actif du bruit (ANC) avec filtre de modélisation de chemin de rétroaction en ligne (FBPM) base l'algorithme du moindre carré moyen filtré (FxLMS). Dans l'algorithme FxLMS, l'erreur de FBPM influence un filtre de contrôle, qui génère un filtre anti-bruit et de modélisation de chemin secondaire (SPM). Le filtre de contrôle diverge lorsque l'erreur est trop importante. Par conséquent, il est difficile pour l’algorithme FxLMS de suivre le chemin du retour sans divergence. D'autre part, l'approche proposée converge de manière stable car l'erreur du filtre FBPM n'influence pas un filtre de contrôle sur le système PIANC. Ainsi, la méthode proposée peut réduire le bruit tout en suivant le chemin de rétroaction. Cet article a vérifié l'efficacité de la méthode proposée par analyse de convergence, simulation informatique et mise en œuvre d'un processeur de signal numérique.
Keisuke OKANO
Tottori University
Naoto SASAOKA
Tottori University
Yoshio ITOH
Tottori University
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Keisuke OKANO, Naoto SASAOKA, Yoshio ITOH, "Feedback Path-Tracking Pre-Inverse Type Active Noise Control" in IEICE TRANSACTIONS on Fundamentals,
vol. E104-A, no. 7, pp. 954-961, July 2021, doi: 10.1587/transfun.2020EAP1081.
Abstract: We propose online feedback path modeling with a pre-inverse type active noise control (PIANC) system to track the fluctuation stably in the feedback path. The conventional active noise control (ANC) system with online feedback path modeling (FBPM) filter bases filtered-x least mean square (FxLMS) algorithm. In the FxLMS algorithm, the error of FBPM influences a control filter, which generates an anti-noise, and secondary path modeling (SPM) filter. The control filter diverges when the error is too large. Therefore, it is difficult for the FxLMS algorithm to track the feedback path without divergence. On the other hand, the proposed approach converges stably because the FBPM filter's error does not influence a control filter on the PIANC system. Thus, the proposed method can reduce noise while tracking the feedback path. This paper verified the effectiveness of the proposed method by convergence analysis, computer simulation, and implementation of a digital signal processor.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/transfun.2020EAP1081/_p
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@ARTICLE{e104-a_7_954,
author={Keisuke OKANO, Naoto SASAOKA, Yoshio ITOH, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Feedback Path-Tracking Pre-Inverse Type Active Noise Control},
year={2021},
volume={E104-A},
number={7},
pages={954-961},
abstract={We propose online feedback path modeling with a pre-inverse type active noise control (PIANC) system to track the fluctuation stably in the feedback path. The conventional active noise control (ANC) system with online feedback path modeling (FBPM) filter bases filtered-x least mean square (FxLMS) algorithm. In the FxLMS algorithm, the error of FBPM influences a control filter, which generates an anti-noise, and secondary path modeling (SPM) filter. The control filter diverges when the error is too large. Therefore, it is difficult for the FxLMS algorithm to track the feedback path without divergence. On the other hand, the proposed approach converges stably because the FBPM filter's error does not influence a control filter on the PIANC system. Thus, the proposed method can reduce noise while tracking the feedback path. This paper verified the effectiveness of the proposed method by convergence analysis, computer simulation, and implementation of a digital signal processor.},
keywords={},
doi={10.1587/transfun.2020EAP1081},
ISSN={1745-1337},
month={July},}
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TY - JOUR
TI - Feedback Path-Tracking Pre-Inverse Type Active Noise Control
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 954
EP - 961
AU - Keisuke OKANO
AU - Naoto SASAOKA
AU - Yoshio ITOH
PY - 2021
DO - 10.1587/transfun.2020EAP1081
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E104-A
IS - 7
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - July 2021
AB - We propose online feedback path modeling with a pre-inverse type active noise control (PIANC) system to track the fluctuation stably in the feedback path. The conventional active noise control (ANC) system with online feedback path modeling (FBPM) filter bases filtered-x least mean square (FxLMS) algorithm. In the FxLMS algorithm, the error of FBPM influences a control filter, which generates an anti-noise, and secondary path modeling (SPM) filter. The control filter diverges when the error is too large. Therefore, it is difficult for the FxLMS algorithm to track the feedback path without divergence. On the other hand, the proposed approach converges stably because the FBPM filter's error does not influence a control filter on the PIANC system. Thus, the proposed method can reduce noise while tracking the feedback path. This paper verified the effectiveness of the proposed method by convergence analysis, computer simulation, and implementation of a digital signal processor.
ER -