Abstract:The existing Generative Fixed-Filter Active Noise Control (GFANC) method generates a suitable control filter based on the current noise frame. This reactive design aims to estimate a control filter that is optimal for the present frame rather than the upcoming one. Consequently, it suffers from an inherent tracking lag and lacks the predictive capability to handle rapidly varying noises. To address this limitation, we propose the Predictive Fixed-Filter Active Noise Control (PFANC) method with a proactive control paradigm in this paper. In the PFANC method, multiple consecutive noise frames are processed by a Convolutional Recurrent Neural Network (CRNN) to predict the next-frame control filter. By utilizing temporal correlations across noise frames to anticipate the control filter in advance, the PFANC method can effectively track dynamic noise changes. Furthermore, the theoretical analysis based on a higher-order Markov formulation shows that incorporating multiple noise frames enhances the estimation of the control filter. Numerical simulations with linear and logarithmic chirp signals, as well as real-world dynamic noises, validate the effectiveness of the PFANC method and its superiority over GFANC and its variations. The PFANC method also exhibits good transferability across different acoustic paths.
From: Zhengding Luo [view email]
[v1]
Sat, 6 Jun 2026 13:35:34 UTC (24,165 KB)
[v2]
Mon, 14 Sep 2026 15:54:13 UTC (25,534 KB)
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