Designing CNN that does one column convolution across the x-axis I am currently working on designing a certain number of CNN for extracting features from images. 

The images are spectogram, and each have a shape being (276,x,3).
X is here the number of column, which incidently also the length of the feature vector that should  be created. 

So the cnn somehow has to use a kernel that has to be 276 rows and 1 column wide, but is it possible in keras to make a 2d kernel and perform 1d convolution. The most important factor is the 1d convolution and the shape of the 2d kernel, as it is being used to alter the importance of some of the entries in the spectogram.
Designing CNN that does one column convolution across the x-axis
So the cnn somehow has to use a kernel that has to be 276 rows and 1 column wide, but is it possible in keras to make a 2d kernel and perform 1d convolution.

I'm not sure I understand your question correctly, but if you have input images that are 276 high, X=500 wide and have 3 color channels, then the following performs column-wise convolutions across the X-axis:
rows, cols, chans = 276, 500, 3

...
model.add(Convolution2D(64, rows, 1, input_shape=(rows, cols, chans)))
...

The output shape of this layer will be (1, 500, 64).
I inserted &quot;64&quot; as the number of filters.. you can set that to whatever you need / whatever works best.
Note: shapes are specified in tensorflow mode (i.e. the order is (rows, cols, channels)). Depending on your configuration you might have to change those shapes to theano mode: (channels, rows, cols).

So the cnn somehow has to use a kernel that has to be 276 rows and 1 column wide,

It actually has to use a kernel that is 276 rows, 1 column and 3 channels wide since your input images have 3 channels.. it would be 276 rows and 1 column wide if you used grayscale images.. but that shouldn't matter.