I'm not entirely clear what you're asking, but, let's say your convolutional input is $m$ channels of $n$ numbers. Assuming a same convolution. Then you output $d$ (maybe not the same as $m$) channels of $n$ numbers.
Do each of the $d$ output channels come from different filters with different weights? yes of course or they would be redundant. Are the same weights used for each of the $m$ input channels in each filter? no, not shared, so it can learn from each channel separately.
Is the convolutional filter the same across the $n$ numbers, when mapping input to output features? yes. Same filter weights are used across the input.