Here,an Active Noise Control (ANC) approach is proposed which replaces Filtered-x Least Mean Square (FxLMS) algorithm with Dual-decoder Convolutional Recurrent Network (DCRN).Due to the importance of phase information in ANC,the input feature of DCRN is the complex spectrogram of the noise signal (including real and imaginary spectrograms).In the network structure,a coding module is used to extract features from the noise complex spectrograms,and a dual-decoder module is used to estimate the real and imaginary spectrograms of the network output.Parameter sharing mechanism and group strategy are adopted to reduce the number of training parameters and improve the learning ability and generalization performance.Especially for wind noise,a new loss function is adopted and the training data are regularized to improve the performance of DCRN.Experiments in both simulation and ANC headphone environments show that the DCRN approach exhibits good noise reduction performance and robustness for both general noise and wind noise.