Performing 1d convolution using a 2d kernel

[Loser]

I a currently trying to perform a 1d convolution on a image using a 2d kernel. The 1d convolution should be performed across the X-axis of the image, the images are spectograms of audio files.  The image in which the convolution in performed is divided in to 13 section, each section centers around a center frequency, in which 13 different CNN network will perform 1d convolution to extract in total (x,13) feature points, and individually (x,1) feature points. x is given by the length of `data_output_train`

The kernel size is defined as such the center element will be the center frequency. 

This is how the data is currently stored:

 

    def model(feature,train_input_data,
            train_output_data,
            test_input_data,
            test_output_data):


            #Feature number 0-12
            number_of_filters = 1
            #Debug stuff!
            print feature
            print center_freq[feature]
            print center_freq[feature]*2
            print int(center_freq[feature]*2)
            print frequency_to_pixel_row(int(center_freq[feature]*2))
   
            col = int(frequency_to_pixel_row(int(center_freq[feature]*2)))
            #init Data containers
            data_train_input = []
            data_test_input = []
            data_train_output = []
            data_test_output = []
            print "jghkjhk"
           
            #Extract the desired section
            for images in train_input_data:
                if feature == 0:
                    #print images.shape[0]
                    data_train_input = images[images.shape[0] - int(center_freq[feature]*2):images.shape[0],:]
                    #print data.shape
            #Vstack so it can be used in keras
            data_train_input_vstack = np.vstack(data_train_input)
           
            #Extract the desired section
            for images in test_input_data:
                if feature == 0:
                    #print images.shape[0]
                    data_test_input = images[images.shape[0] - int(center_freq[feature]*2):images.shape[0],:]
                    #print data.shape
            data_test_input_vstack = np.vstack(data_test_input)
           
            #Extract the desired output data
            for rows in train_output_data:
                #print rows.shape
                data_train_output = rows[:,feature]
            data_train_output_vstack = np.vstack(data_train_output)
   
            #Extract the desired output data
            for rows in test_output_data:
                #print rows.shape
                data_test_output = rows[:,feature]
            data_test_output_vstack = np.vstack(data_test_output)

    

Now i need to define the network structure such that it performs the 1d convolution.. But how do i ensure it also does that? and performs the way i want it to do.. I guess i have to use convolution2d, but how do i fix it to only perform 1d convoulution?

[Daπid]

Why don't you use 1D convolution? It works on 2D arrays (time,
frequency), as a 2D convolution works on 3D arrays (x, y, colour).

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[Daπid]

And by that I meant the Convolution1D layer.
https://keras.io/layers/convolutional/#convolution1d

[nof...@gmail.com]

is it possible to it with a kernel?

[Loser]

a 2d kernel

[nof...@gmail.com]

Oh.. i do have (x,y,color)..