Practical active noise control (ANC) systems, like the active noise cancellation headphone, usually adopt a control filter with preset coefficients to achieve satisfactory noise reduction performance for dynamic noise and higher robustness. In this strategy, selecting the appropriate control filter for different types of noise is critical to the noise cancellation performance, and this selection mechanism is typically determined by trial and error. Hence, this article proposes a computation-efficient one-dimensional convolutional neural network capable of selecting the most suitable pre-trained control filter for each distinct primary noise. Applying the similarity matching method allows the proposed model to have a better generalization and can even deal with zero-shot noise, whose class does not exist in the training set. The Large-margin softmax (L-softmax) is also investigated to improve the proposed model's performance. Furthermore, when dealing with the N-shot learning problem, where there are few known real-world noise samples for the ANC system, an additional fine-tuning strategy is used to improve control filter selection accuracy. Numerical simulations on measured primary and secondary paths validate the proposed method's efficacy.

Selective Fixed-filter Active Noise Control (SFANC) is limited by its selection of a single candidate from pre-trained control filters. In contrast, Generative Fixed-filter Active Noise Control (GFANC) addresses this limitation by employing an adaptive combination of sub control filters to generate more suitable control filters for different primary noises. However, GFANC solely relies on the information from the current noise frame to generate its control filter, resulting in potential inaccuracies when dealing with dynamic noises. Therefore, we propose a GFANC-Kalman approach that integrates an efficient one-dimensional convolutional neural network (1D CNN) with a Kalman filter to further improve the performance of GFANC. Specifically, the weight vector used to combine sub control filters is predicted by the 1D CNN for each noise frame, and then processed by the Kalman filter with minimal complexity. By considering the correlation between adjacent noise frames, the Kalman filter can enhance the accuracy and robustness of weight vector prediction. Hence, GFANC-Kalman is more able to adapt to changes in noise distribution, particularly for dynamic noises. Numerical simulations validate the efficacy of the proposed GFANC-Kalman approach in dealing with real-world dynamic noises.

Output saturation, mainly caused by the power amplifier, is a critical issue influencing the performance and stability of an adaptive system, such as in active noise control. In this paper, a quadratically constrained quadratic program (QCQP) is defined to achieve optimal control under the averaging-output-power constraint, which ensures the output of the system operates linearly and hence, avoids the output saturation. To solve this QCQP problem recursively in practice, this paper utilizes one of the leaky-based filtered-x least mean square algorithm with an optimal leak factor. However, this method only can be applied when the statistical feature of the control signal with maximum output-power is known, which is difficult to obtain in practice. Hence, by incorporating the adaptive inverse modeling technique, we can derive a practical estimation of the optimal leaky factor, which is applicable to different noise types. Furthermore, as the optimal output-constraint control forces the output to operate linearly, the nonlinear amplifier model is not required for the leak factor estimate. The simulation of the proposed algorithm is carried out on measured nonlinear paths to validate its efficacy.