Combining Convolution Neural Network
One way to combine already trained models would be to use a common fully-connected layer and train the network.
You can place this fully-connected layer at the end of both the convolutional models.
So, input will go into ConVModel-1 and ConvModel-2. You will get 2 output vectors. Combine these 2 output vectors (concatenate, average, etc.). Now pass this newly formed vector to the fully connected layer.
You can now train this network in 2 ways -

Update weights for only the FC layer.
Update weights for FC layer + both the ConvModels.
I am working on Audio Classification and using Spectrograms and MFCC plots im order to train CNN image classifier. Currently, I have two separate ConvNets trained on these features giving average ( 55-60% accuracy ). I have two separate weight files for each model.
Now I want to combine the two models i.e. I want to extract Spectrograms and MFCC from each audio file and test on my already built models and get higher accuracy. How can I do that?