Let's say I have two channels that I wish to feed into a CNN. One of the channel contains 4 traces and has a width of 512. Stacking them on top of each other therefore yields an image with dimensions (4, 512). The other channel is just 1 trace, so its dimensions would be (1, 512).
I then have convolutional filters that are of dimension (1, 5) as an example. That means that the filters run over each trace separately. The first channel (containing the 4 traces) will then have a set of filter weights, shared among the 4 traces. The second channel (containing the 1 trace) will have a completely different set of weights (as per this SE question).
TLDR: Can convolutional layers in a CNN have different dimensions? Putting this in the context of images: Could we have a CNN that takes an image that has dimensions (100, 100) for the red channel, (100, 100) for the green channel, and (50, 100) for the blue channel?