Yes, usually you would increase the filters at the beginning at least.
The number of filters for 1D and 2D convolutions are defined in the same sense.
I think it depends on your use case, if a channel reduction at the beginning will work fine or not.
E.g. if your input data contains some redundant channels, it might work fine, otherwise you might lose too much information.
Have you tried different approaches and see a specific pattern in the model performance regarding the number of channels?