An active noise control (ANC) method is introduced 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 the DCRN is the complex spectrogram of the noise signal (including the 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 respectively. Parameter sharing mechanism and group strategy are adopted to reduce the number of training parameters and improve the learning ability and generalization ability. Especially for wind noise, a new loss function is selected and the training data is regularized to improve the performance of DCRN. The experiments in both simulation and ANC headphone environments show that the DCRN method exhibits good noise reduction performance and robustness for general noise and wind noise.