A Deep Learning Approach to Active Noise Control

We formulate active noise control (ANC) as a supervised learning problem and propose a deep learning approach, called deep ANC, to address the nonlinear ANC problem. A convolutional recurrent network (CRN) is trained to estimate the real and imaginary spectrograms of the canceling signal from the reference signal so that the corresponding anti-noise can eliminate or attenuate the primary noise in the ANC system. Large-scale multi-condition training is employed to achieve good generalization and robustness against a variety of noises. The deep ANC method can be trained to achieve active noise cancellation no matter whether the reference signal is noise or noisy speech. In addition, a delay-compensated strategy is introduced to address the potential latency problem of ANC systems. Experimental results show that the proposed method is effective for wide-band noise reduction and generalizes well to untrained noises. Moreover, the proposed method can be trained to achieve ANC within a quiet zone.