A Hybrid Deep-Online Learning Based Method for Active Noise Control in Wave Domain

Abstract

The traditional feedback Active Noise Control (ANC) algorithms are built upon linear filters, which leads to reduced performance when dealing with real-world noise. Deep learning-based feedback ANC algorithms have been proposed to overcome this problem. However, methods relying on pre-trained neural networks exhibit performance degradation when encountering noise from unseen scenes in the training dataset. This paper proposed a hybrid deep-online learning based spatial ANC system which combines online learning with pre-trained deep neural networks. The proposed method can keep the performance on noise from the trained scenes while improve the performance of cancelling noise from new scenes. Additionally, by incorporating wave domain decomposition, this paper achieves noise cancellation over a control spatial region. Simulation experiments validate the effectiveness of the combination of online learning and deep learning in handling previously unseen noise. Furthermore, the efficiency of wave domain decomposition in spatial noise cancellation is also verified.