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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2504.04450 |
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| _version_ | 1866910925848576000 |
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| author | Bai, Lu Li, Mengtong Lian, Siyuan Chen, Kai Lu, Jing |
| author_facet | Bai, Lu Li, Mengtong Lian, Siyuan Chen, Kai Lu, Jing |
| contents | Active Noise Control (ANC) systems are challenged by nonlinear distortions, which degrade the performance of traditional adaptive filters. While deep learning-based ANC algorithms have emerged to address nonlinearity, existing approaches often overlook critical limitations: (1) end-to-end Deep Neural Network (DNN) models frequently violate causality constraints inherent to real-time ANC applications; (2) many studies compare DNN-based methods against simplified or low-order adaptive filters rather than fully optimized high-order counterparts. In this letter, we propose a causality-preserving time-domain ANC framework that synergizes WaveNet with Volterra Neural Networks (VNNs), explicitly addressing system nonlinearity while ensuring strict causal operation. Unlike prior DNN-based approaches, our method is benchmarked against both state-of-the-art deep learning architectures and rigorously optimized high-order adaptive filters, including Wiener solutions. Simulations demonstrate that the proposed framework achieves superior performance over existing DNN methods and traditional algorithms, revealing that prior claims of DNN superiority stem from incomplete comparisons with suboptimal traditional baselines. Source code is available at https://github.com/Lu-Baihh/WaveNet-VNNs-for-ANC.git. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_04450 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | WaveNet-Volterra Neural Networks for Active Noise Control: A Fully Causal Approach Bai, Lu Li, Mengtong Lian, Siyuan Chen, Kai Lu, Jing Audio and Speech Processing Active Noise Control (ANC) systems are challenged by nonlinear distortions, which degrade the performance of traditional adaptive filters. While deep learning-based ANC algorithms have emerged to address nonlinearity, existing approaches often overlook critical limitations: (1) end-to-end Deep Neural Network (DNN) models frequently violate causality constraints inherent to real-time ANC applications; (2) many studies compare DNN-based methods against simplified or low-order adaptive filters rather than fully optimized high-order counterparts. In this letter, we propose a causality-preserving time-domain ANC framework that synergizes WaveNet with Volterra Neural Networks (VNNs), explicitly addressing system nonlinearity while ensuring strict causal operation. Unlike prior DNN-based approaches, our method is benchmarked against both state-of-the-art deep learning architectures and rigorously optimized high-order adaptive filters, including Wiener solutions. Simulations demonstrate that the proposed framework achieves superior performance over existing DNN methods and traditional algorithms, revealing that prior claims of DNN superiority stem from incomplete comparisons with suboptimal traditional baselines. Source code is available at https://github.com/Lu-Baihh/WaveNet-VNNs-for-ANC.git. |
| title | WaveNet-Volterra Neural Networks for Active Noise Control: A Fully Causal Approach |
| topic | Audio and Speech Processing |
| url | https://arxiv.org/abs/2504.04450 |