ISCA Archive Interspeech 2021
ISCA Archive Interspeech 2021

A Deep Learning Method to Multi-Channel Active Noise Control

Hao Zhang, DeLiang Wang

This paper addresses multi-channel active noise control (MCANC) on the basis of deep ANC, which performs active noise control by employing deep learning to encode the optimal control parameters corresponding to different noises and environments. The proposed method trains a convolutional recurrent network (CRN) to estimate the real and imaginary spectrograms of all the canceling signals simultaneously from the reference signals so that the corresponding anti-noises cancel or attenuate the primary noises in an MCANC system. We evaluate the proposed method under multiple MCANC setups and investigate the impact of the number of canceling loudspeakers and error microphones on the overall performance. Experimental results show that deep ANC is effective for MCANC in various scenarios. Moreover, the proposed method is robust against untrained noises and works well in the presence of loudspeaker nonlinearity.

doi: 10.21437/Interspeech.2021-1512

Cite as: Zhang, H., Wang, D. (2021) A Deep Learning Method to Multi-Channel Active Noise Control. Proc. Interspeech 2021, 681-685, doi: 10.21437/Interspeech.2021-1512

  author={Hao Zhang and DeLiang Wang},
  title={{A Deep Learning Method to Multi-Channel Active Noise Control}},
  booktitle={Proc. Interspeech 2021},