Research and application of the algorithm editing method for improving active noise control performance
In the field of active noise control systems, combining multiple algorithms to improve equalization performance has generated notable interest. Nevertheless, the computation of threshold control parameters for algorithm switching during each iteration leads to a significant increase in computational...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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SAGE Publishing
2025-09-01
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| Series: | Journal of Low Frequency Noise, Vibration and Active Control |
| Online Access: | https://doi.org/10.1177/14613484251320198 |
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| _version_ | 1849339140910350336 |
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| author | Haisheng Song Yahui Dong Na Yang Zhiyong Chen |
| author_facet | Haisheng Song Yahui Dong Na Yang Zhiyong Chen |
| author_sort | Haisheng Song |
| collection | DOAJ |
| description | In the field of active noise control systems, combining multiple algorithms to improve equalization performance has generated notable interest. Nevertheless, the computation of threshold control parameters for algorithm switching during each iteration leads to a significant increase in computational complexity. Additionally, the frequent switching of algorithms poses challenges in fully leveraging the performance of individual algorithms. Therefore, this paper utilizes the film editing concept to introduce the Algorithm Editing Method (AEM). AEM entails selecting appropriate algorithms based on distinctive iteration stages, followed by precise cutting and splicing according to the defined switching coefficient. Appropriate algorithms are implemented at specific iteration stages, eliminating the requirement for frequent algorithm switching during the iterative process. To substantiate the effectiveness of this approach, the FxLMS and Momentum-FxLMS algorithms serve as foundational components of AEM, enhancing convergence performance in an active noise control system. The results show a noteworthy enhancement in convergence speed and reduction of steady-state error, attained without a simultaneous escalation in computational complexity. Additionally, this study extends the application of AEM to improve the system’s robustness against impulse signals. The simulations and tests results demonstrate the method’s effectiveness, achieving a balance of optimized convergence speed, reduction in steady-state error, and minimized computational complexity. |
| format | Article |
| id | doaj-art-4b2f04ba82b34b45ab0f51687f8776e5 |
| institution | Kabale University |
| issn | 1461-3484 2048-4046 |
| language | English |
| publishDate | 2025-09-01 |
| publisher | SAGE Publishing |
| record_format | Article |
| series | Journal of Low Frequency Noise, Vibration and Active Control |
| spelling | doaj-art-4b2f04ba82b34b45ab0f51687f8776e52025-08-20T03:44:13ZengSAGE PublishingJournal of Low Frequency Noise, Vibration and Active Control1461-34842048-40462025-09-014410.1177/14613484251320198Research and application of the algorithm editing method for improving active noise control performanceHaisheng SongYahui DongNa YangZhiyong ChenIn the field of active noise control systems, combining multiple algorithms to improve equalization performance has generated notable interest. Nevertheless, the computation of threshold control parameters for algorithm switching during each iteration leads to a significant increase in computational complexity. Additionally, the frequent switching of algorithms poses challenges in fully leveraging the performance of individual algorithms. Therefore, this paper utilizes the film editing concept to introduce the Algorithm Editing Method (AEM). AEM entails selecting appropriate algorithms based on distinctive iteration stages, followed by precise cutting and splicing according to the defined switching coefficient. Appropriate algorithms are implemented at specific iteration stages, eliminating the requirement for frequent algorithm switching during the iterative process. To substantiate the effectiveness of this approach, the FxLMS and Momentum-FxLMS algorithms serve as foundational components of AEM, enhancing convergence performance in an active noise control system. The results show a noteworthy enhancement in convergence speed and reduction of steady-state error, attained without a simultaneous escalation in computational complexity. Additionally, this study extends the application of AEM to improve the system’s robustness against impulse signals. The simulations and tests results demonstrate the method’s effectiveness, achieving a balance of optimized convergence speed, reduction in steady-state error, and minimized computational complexity.https://doi.org/10.1177/14613484251320198 |
| spellingShingle | Haisheng Song Yahui Dong Na Yang Zhiyong Chen Research and application of the algorithm editing method for improving active noise control performance Journal of Low Frequency Noise, Vibration and Active Control |
| title | Research and application of the algorithm editing method for improving active noise control performance |
| title_full | Research and application of the algorithm editing method for improving active noise control performance |
| title_fullStr | Research and application of the algorithm editing method for improving active noise control performance |
| title_full_unstemmed | Research and application of the algorithm editing method for improving active noise control performance |
| title_short | Research and application of the algorithm editing method for improving active noise control performance |
| title_sort | research and application of the algorithm editing method for improving active noise control performance |
| url | https://doi.org/10.1177/14613484251320198 |
| work_keys_str_mv | AT haishengsong researchandapplicationofthealgorithmeditingmethodforimprovingactivenoisecontrolperformance AT yahuidong researchandapplicationofthealgorithmeditingmethodforimprovingactivenoisecontrolperformance AT nayang researchandapplicationofthealgorithmeditingmethodforimprovingactivenoisecontrolperformance AT zhiyongchen researchandapplicationofthealgorithmeditingmethodforimprovingactivenoisecontrolperformance |