Appropriate convolutional neural network architecture when the input consists of two distinct signals I have a dataset consisting of a set of samples. Each sample consists of two distinct desctized signals S1(t), S2(t). Both signals are synchronous; however, they show different aspects of a phenomena.
I want to train a Convolutional Neural Network, but I don't know which architecture is appropriate for this kind of data. 
I can consider two channels for input, each corresponding to one of the signals. But, I don't think convolving two signals can produce appropriate features.
I believe the best way is to process each signal separately in the first layers, then join them in the classification layers in the final step. How can I achieve this? What architecture should I use?
I don't know what you mean by desctized signals but if I understand your question correctly, separating two signal and passing them through same architecture of CNN (even with different parameters) is not a good idea. Because when they are together (as different channels) they will be treated differently by the CNN (each channel has its own parameters) and even this way the network is able to combine these two signals and get better results by information extracted from this combination.