# One-Dimensional Convolutional Neural Network

Can someone explain how 'One-Dimensional Convolutional Neural Network' works. I do understand the 2-D for image but for 1-D how is the filer created. is it fixed 1-D filter within a specific time interval or the operation is the same as we convolve a signal with a filter in signal processing y = f*x

The below image shows a filter set with shared parameter $$W$$ covering the overlapping regions of the input.
By shared parameter we mean $$f_i=\mbox{ReLU}(\mbox{sum}(W \odot \mbox{region}_i))$$, where $$\odot$$ is a point-wise product between a region of input and $$W$$.