Yep, neural networks are usually described by the kernel size, padding, stride and so on… Such a strange work the one you found.
All the pytorch layers expect a batch dimension. If you have a single 1d sample, it has to be of shape NxCxT.
So:
1D signals–> NxCxT usually c=1 for mono audio or time series, c=2 for dual audio
2D signals → NxCxHxW usuall c=1 for b&w images, c=3 for color…
fully conected (linear layers)–> Nx*xT It expects the first dimension to be batch, then you can have as many dimensions as you want (it will recursively apply the fc among them all like in a nested for loop)
So basically the, for a 1D signal, if you apply a convolution to a batched tensor NxCxT
it’s equivalent to do a for loop from n=0 to n=N. They are independent.
In the papers you don’t describe the batch dimension, still both pytorch and tensorflow use it in practice.