A computer-implemented method of separating audio signals from different speech sources, comprising: receiving, by a processor, a mixture audio signal comprising audio signals from a plurality of speech sources; transforming, by the processor, the mixture audio signal into an encoded representation by an encoder convolutional neural network with multiple convolutional layers and nonlinear activation, wherein transforming the mixture audio signal into an encoded representation by an encoder convolutional neural network comprises: transforming, by the processor, the mixture audio signal into an intermediate encoded representation using at least a first convolutional layer, the intermediate encoded representation being represented by N dimensions; transforming, by the processor, the intermediate encoded representation to the encoded representation using at least three subsequent convolutional layers, each layer implementing one-dimensional convolutional operation with N learnable kernels and outputting an output representation with N dimensions, wherein the output representation of the final layer of said at least three convolutional layers is the encoded representation, wherein said at least three subsequent convolutional layers have nonlinear activation; separating the encoded representation into a plurality of individual representations corresponding to the plurality of speech sources; transforming the plurality of individual representations into a plurality of audio signals corresponding to the plurality of speech sources by a decoder CNN, the structure of the decoder CNN corresponding to the structure of the encoder CNN, wherein the decoder CNN comprises at least three subsequent decoder convolutional layers and a final decoder convolutional layer, each of said at least three subsequent decoder convolutional layers implementing one-dimensional convolutional operation and nonlinear activation. receiving, by a processor, a mixture audio signal comprising audio signals from a plurality of speech sources; transforming, by the processor, the mixture audio signal into an encoded representation by an encoder convolutional neural network with multiple convolutional layers and nonlinear activation, wherein transforming the mixture audio signal into an encoded representation by an encoder convolutional neural network comprises: transforming, by the processor, the mixture audio signal into an intermediate encoded representation using at least a first convolutional layer, the intermediate encoded representation being represented by N dimensions; transforming, by the processor, the intermediate encoded representation to the encoded representation using at least three subsequent convolutional layers, each layer implementing one-dimensional convolutional operation with N learnable kernels and outputting an output representation with N dimensions, wherein the output representation of the final layer of said at least three convolutional layers is the encoded representation, wherein said at least three subsequent convolutional layers have nonlinear activation; transforming, by the processor, the mixture audio signal into an intermediate encoded representation using at least a first convolutional layer, the intermediate encoded representation being represented by N dimensions; transforming, by the processor, the intermediate encoded representation to the encoded representation using at least three subsequent convolutional layers, each layer implementing one-dimensional convolutional operation with N learnable kernels and outputting an output representation with N dimensions, wherein the output representation of the final layer of said at least three convolutional layers is the encoded representation, wherein said at least three subsequent convolutional layers have nonlinear activation; separating the encoded representation into a plurality of individual representations corresponding to the plurality of speech sources; transforming the plurality of individual representations into a plurality of audio signals corresponding to the plurality of speech sources by a decoder CNN, the structure of the decoder CNN corresponding to the structure of the encoder CNN, wherein the decoder CNN comprises at least three subsequent decoder convolutional layers and a final decoder convolutional layer, each of said at least three subsequent decoder convolutional layers implementing one-dimensional convolutional operation and nonlinear activation.