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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2601.15889 |
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| _version_ | 1866917435921137664 |
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| author | Luo, Zhengding Ma, Haozhe Wang, Boxiang Yang, Ziyi Shi, Dongyuan Gan, Woon-Seng |
| author_facet | Luo, Zhengding Ma, Haozhe Wang, Boxiang Yang, Ziyi Shi, Dongyuan Gan, Woon-Seng |
| contents | The Filtered-x Normalized Least Mean Square (FxNLMS) algorithm suffers from slow convergence and a risk of divergence, although it can achieve low steady-state errors after sufficient adaptation. In contrast, the Generative Fixed-Filter Active Noise Control (GFANC) method offers fast response speed, but its lack of adaptability may lead to large steady-state errors. This paper proposes a hybrid GFANC-FxNLMS algorithm to leverage the complementary advantages of both approaches. In the hybrid GFANC-FxNLMS algorithm, GFANC provides a frame-level control filter as an initialization for FxNLMS, while FxNLMS performs continuous adaptation at the sampling rate. Small variations in the GFANC-generated filter may repeatedly reinitialize FxNLMS, interrupting its adaptation process and destabilizing the system. An online clustering module is introduced to avoid unnecessary re-initializations and improve system stability. Simulation results show that the proposed algorithm achieves fast response, very low steady-state error, and high stability, requiring only one pre-trained broadband filter. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_15889 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | A Stabilized Hybrid Active Noise Control Algorithm of GFANC and FxNLMS with Online Clustering Luo, Zhengding Ma, Haozhe Wang, Boxiang Yang, Ziyi Shi, Dongyuan Gan, Woon-Seng Audio and Speech Processing Sound The Filtered-x Normalized Least Mean Square (FxNLMS) algorithm suffers from slow convergence and a risk of divergence, although it can achieve low steady-state errors after sufficient adaptation. In contrast, the Generative Fixed-Filter Active Noise Control (GFANC) method offers fast response speed, but its lack of adaptability may lead to large steady-state errors. This paper proposes a hybrid GFANC-FxNLMS algorithm to leverage the complementary advantages of both approaches. In the hybrid GFANC-FxNLMS algorithm, GFANC provides a frame-level control filter as an initialization for FxNLMS, while FxNLMS performs continuous adaptation at the sampling rate. Small variations in the GFANC-generated filter may repeatedly reinitialize FxNLMS, interrupting its adaptation process and destabilizing the system. An online clustering module is introduced to avoid unnecessary re-initializations and improve system stability. Simulation results show that the proposed algorithm achieves fast response, very low steady-state error, and high stability, requiring only one pre-trained broadband filter. |
| title | A Stabilized Hybrid Active Noise Control Algorithm of GFANC and FxNLMS with Online Clustering |
| topic | Audio and Speech Processing Sound |
| url | https://arxiv.org/abs/2601.15889 |