Deep MCANC: A deep learning approach to multi-channel active noise control

Abstract

Traditional multi-channel active noise control (MCANC) is based on adaptive filtering and usually uses a separate control unit for each channel. This paper introduces a deep learning based approach for multi-channel active noise control (ANC). The proposed approach, called deep MCANC, encodes optimal control parameters corresponding to different noises and environments, and jointly computes the multiple canceling signals to cancel or attenuate the primary noises captured at error microphones. A convolutional recurrent network (CRN) is employed for complex spectral mapping where the summated power of error signals is used as the loss function for CRN training. Deep MCANC is a fixed-parameter ANC approach and large-scale multi-condition training is employed to achieve robustness against a variety of noises. We explore the performance of deep MCANC with different setups and investigate the impact of factors such as the number of loudspeakers and microphones, and the position of a secondary source, on ANC performance. Experimental results show that deep MCANC is effective for wideband noise reduction and generalizes well to untrained noises. Moreover, the proposed approach is robust against variations in reference signals and works well in the presence of nonlinear distortions.