@googlebot thanks for you help I think I understood it now…
so for the conv1d with kernel size 3 it is like this

the filter with size 5 moving over the time steps. and depending on how “high” the next matrix should be the more filters we use. so for 8 output channels 8 filters etc.
now I’m thinking of the correlation convolution. just imagine there would be 3 acel matrices of three different objects…  like in the image below. with a [mx1] kernel I want to go over each input channel of the three elements. I would pad one matrix m0 and one m4 so that I would be able to get 3 output matrices.
image1304×718 14 KB
so as in the picture. input would be (3,3,5) and a possible output (3,3,5). by that having 3 filters. I’m just unsure if this would be possible.
I was also considering to just “.transpose(0,2)” the matrix. so that the batch_size becomes the time steps and the time steps the batch size. and then use a regular conv1d with kernel size m the previously batch size and now time steps.
Hope I could make it somehow understandable. Do you think this would be possible without transposing?