Attention based Convolutional Neural Network for Active Noise Control

Active noise control (ANC) is a technology that uses sound waves to reduce or eliminate unwanted ambient noise in a given environment. We approached ANC using a deep neural network consisting of convolutional and attention layers, followed by deconvolutional layers. The proposed deep learning model processes the acoustic signal samples in frames. A piece-wise non-linear function is employed to represent the secondary path and loudspeaker saturation non-linearies. To assess the efficacy of the proposed model, we conducted tests using filtered white noise across different frequency bands and various tonals from a diverse database. The proposed convolutional model with attention layers yields a normalized mean square error (NMSE) recuction of upto 3 dB more as compared to that achieved using just convolutional model. Experiment results are presented for both confined and anechoic environments, providing a comprehensive evaluation of the proposed ANC approach.