Transferable Latent of CNN-Based Selective Fixed-Filter Active Noise Control

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.

Automated summary

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, which can improve noise reduction performance. The proposed model can even handle zero-shot noise and has better generalization using the similarity matching method. The Large-margin softmax (L-softmax) is investigated to enhance the model's performance, and an additional fine-tuning strategy is used for control filter selection accuracy in the N-shot learning problem with few known real-world noise samples. Numerical simulations validate the efficacy of the proposed method based on measured primary and secondary paths.