how does stride work in denosing neural network

I am studying the convolutional network in this article, https://www.mathworks.com/help/deeplearning/ug/denoise-speech-using-deep-learning-networks.html

The input image size is [129 8]. There are 16 cnn layers, some have [9 1] kernel and some have [5 1]. Let use [9 1] kernel. In one computation, cnn will compute a dot product of 9 vertival pixels of the input image if I understand them correctly. The stride is set to [1 100], this mean the kernel moves 1 pixel vertivally and 100 pixels horizontally. However, the input is 129x8.

Answer 1

[konoha]

the first cnn layer has kernel [9 8]. This has scanned through the input matrix and output is a vector. a [1 100] works as designed, which move vertically. But the choice of moving 100 horizontally is remained unclear.