Why is this not ordinary convolution?
It's analogous to an extremely specific convolution step on a 2D image. That is, it's analogous to an $N \times I$ image with one feature map (e.g. a black &amp; white image, if we're talking about the input), and you choose to use $J$ filters of size $F \times I$ which spans the entire width of the image and only strides along the length to create $J$ feature maps of size $(N - F + 1) \times 1.$ The restrictions here are that:

-The "image" (or current layer you're working on) necessarily only has one feature map

-The filters span the entire width of the image, so they don't stride along that direction, and the resulting feature maps have a width of $1.$

You could then re-interpret the $(N-F+1) \times 1$ resulting feature maps as a single $(N-F+1) \times J$ image, which then creates the exact same restrictions for the next convolutional layer (or more restrictions if you're doing pooling).

So yes, the analogy is there, but it's only analogous to a very restricted class of convolution on 2D image arrays, which I don't think is very useful. The kind of application it would be used for is black and white images for which you don't care about translation invariance along one of the dimensions.

The reason CNNs on images are much more flexible than this analogy allows for is because the input objects (and each respective hidden layer) of an image CNN is a 3D array, not a 2D array as it is in this case. The third dimension is the feature maps (the RGB values for the input image, for example). Then you could allow the filter to be of any size in both dimensions, and stride along both directions. I would prefer to keep the text interpretation when it comes to 2D inputs of a CNN.