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?