Attention-based convolutional neural network for multi-channel active noise control

Multi-channel active noise control systems utilize multiple control units to cancel unwanted noise across different channels simultaneously. By employing adaptive filtering techniques, these systems can effectively reduce noise levels in complex environments, offering improved noise mitigation compared to single-channel active noise control approaches. The disadvantage of adaptive filtering in active noise control is its limited ability to accurately model nonlinearities present in real-world noise environments. In this work, we propose an attention-based convolutional neural network comprising convolutional, deconvolutional, and attention mechanism to approach multi-channel active noise control. The system simultaneously calculates multiple canceling signals to eliminate primary noises captured by error microphones. To evaluate the effectiveness of our proposed model, we conducted testing utilizing filtered white noise spanning various frequency bands. Experimental evaluations across various setups, including different loudspeaker and microphone configurations, as well as variations in secondary source positioning, are used to assess the performance of the proposed approach. Results demonstrate that the proposed attention-based convolutional neural network for multi-channel active noise control yields a noise reduction of 22.65 dB on wideband noise, while the conventional Filtered-x least mean square approach yields a noise reduction of 4.23 dB, indicating the effectiveness of the proposed approach.