An audio separation method of a multi-channel multi-sound source mixed audio signal, performed by a computing device by using an artificial neural network, the method comprising: transforming one of a plurality of channel components of the multi-channel multi-sound source mixed audio signal into a single-channel multi-sound source mixed audio representation in a feature space; performing a two-dimensional dilated convolution on the multi-channel multi-sound source mixed audio signal to extract a plurality of inter-channel features based on a plurality of different values of an inter-channel dilation coefficient z and/or a plurality of different values of an inter-channel stride p and at least one parallel two-dimensional convolutional layer, the inter-channel dilation coefficient is a quantity of intervals of channels taken to calculate the inter-channel features, the inter-channel stride is a stride size by which convolution kernels move when the convolution kernels traversed an input data matrix, the multi-channel multi-sound source mixed audio signal comprising C channel components, each channel component comprising S sampling points, C and S being integers greater than or equal to 2, each two-dimensional convolutional layer comprising corresponding n two-dimensional convolution kernels of the same size, a length of each two-dimensional convolution kernel being less than or equal to S, for each value of the inter-channel dilation coefficient z and/or each value of the inter-channel stride p, each two-dimensional convolutional layer performing a two-dimensional dilated convolution on the multi-channel multi-sound source mixed audio signal and the corresponding n two-dimensional convolution kernels of the same size to generate corresponding n feature maps as a corresponding inter-channel feature, z and n being integers, 1 ≤ z < C, and n ≥ 1; performing a feature fusion on the single-channel multi-sound source mixed audio representation and the plurality of inter-channel features to obtain a fused multi-channel multi-sound source mixed audio feature; estimating respective weights of a plurality of sound sources in the single-channel multi-sound source mixed audio representation based on the fused multi-channel multi-sound source mixed audio feature; obtaining respective representations of the plurality of sound sources according to the single-channel multi-sound source mixed audio representation and the respective weights; and transforming the respective representations of the plurality of sound sources into respective audio signals of the plurality of sound sources. transforming one of a plurality of channel components of the multi-channel multi-sound source mixed audio signal into a single-channel multi-sound source mixed audio representation in a feature space; performing a two-dimensional dilated convolution on the multi-channel multi-sound source mixed audio signal to extract a plurality of inter-channel features based on a plurality of different values of an inter-channel dilation coefficient z and/or a plurality of different values of an inter-channel stride p and at least one parallel two-dimensional convolutional layer, the inter-channel dilation coefficient is a quantity of intervals of channels taken to calculate the inter-channel features, the inter-channel stride is a stride size by which convolution kernels move when the convolution kernels traversed an input data matrix, the multi-channel multi-sound source mixed audio signal comprising C channel components, each channel component comprising S sampling points, C and S being integers greater than or equal to 2, each two-dimensional convolutional layer comprising corresponding n two-dimensional convolution kernels of the same size, a length of each two-dimensional convolution kernel being less than or equal to S, for each value of the inter-channel dilation coefficient z and/or each value of the inter-channel stride p, each two-dimensional convolutional layer performing a two-dimensional dilated convolution on the multi-channel multi-sound source mixed audio signal and the corresponding n two-dimensional convolution kernels of the same size to generate corresponding n feature maps as a corresponding inter-channel feature, z and n being integers, 1 ≤ z < C, and n ≥ 1; performing a feature fusion on the single-channel multi-sound source mixed audio representation and the plurality of inter-channel features to obtain a fused multi-channel multi-sound source mixed audio feature; estimating respective weights of a plurality of sound sources in the single-channel multi-sound source mixed audio representation based on the fused multi-channel multi-sound source mixed audio feature; obtaining respective representations of the plurality of sound sources according to the single-channel multi-sound source mixed audio representation and the respective weights; and transforming the respective representations of the plurality of sound sources into respective audio signals of the plurality of sound sources.