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| Hauptverfasser: | , , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2026
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| Schlagworte: | |
| Online-Zugang: | https://arxiv.org/abs/2605.00494 |
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| _version_ | 1866913080346148864 |
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| author | Yang, Ziyi Luo, Zhengding Zou, Yisong Wang, Boxiang Huang, Qirui Gan, Woon-Seng |
| author_facet | Yang, Ziyi Luo, Zhengding Zou, Yisong Wang, Boxiang Huang, Qirui Gan, Woon-Seng |
| contents | To address the limitations of existing Generative Fixed-Filter Active Noise Control (GFANC) methods, which rely on filter decomposition and recombination and require supervised learning with labeled data, this paper proposes a Transformer-based End-to-End Control-Filter Generation (E2E-CFG) framework. Unlike previous approaches that predict combination weights of sub control filters, the proposed method directly generates control filters in an unsupervised manner by integrating the co-processor and real-time controller into a fully differentiable ANC system, where the accumulated error signal is used as the training objective. By abandoning the decomposition--reconstruction process, the proposed design simplifies the control pipeline and avoids error accumulation, while the Transformer architecture effectively captures global and dynamic noise characteristics through its attention mechanism. Numerical simulations on real-recorded noises demonstrate that the proposed method achieves improved noise reduction performance and adaptability to different types of noises compared with the original GFANC framework. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_00494 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Transformer-based End-to-End Control Filter Generation for Active Noise Control Yang, Ziyi Luo, Zhengding Zou, Yisong Wang, Boxiang Huang, Qirui Gan, Woon-Seng Audio and Speech Processing To address the limitations of existing Generative Fixed-Filter Active Noise Control (GFANC) methods, which rely on filter decomposition and recombination and require supervised learning with labeled data, this paper proposes a Transformer-based End-to-End Control-Filter Generation (E2E-CFG) framework. Unlike previous approaches that predict combination weights of sub control filters, the proposed method directly generates control filters in an unsupervised manner by integrating the co-processor and real-time controller into a fully differentiable ANC system, where the accumulated error signal is used as the training objective. By abandoning the decomposition--reconstruction process, the proposed design simplifies the control pipeline and avoids error accumulation, while the Transformer architecture effectively captures global and dynamic noise characteristics through its attention mechanism. Numerical simulations on real-recorded noises demonstrate that the proposed method achieves improved noise reduction performance and adaptability to different types of noises compared with the original GFANC framework. |
| title | Transformer-based End-to-End Control Filter Generation for Active Noise Control |
| topic | Audio and Speech Processing |
| url | https://arxiv.org/abs/2605.00494 |