Process melspectrograms with convolutional neural network I am trying to do audio classification with a convolutional neural network. There are six classes. With librosa, I have created melspectrograms for the one second long .wav audio files. It returned 640x480 .jpg files.
My question is now how to proceed with the input, since I think it is too large as input for the network. If so, what would an adequate resolution be? Something around 60x60? Does it even have to be quadratic?

Options from my perspective:


Re-encode melspectrograms from librosa with smaller resolution
Use cv2 and simply do a cv2.resize() before passing it to the input layer.
Leave the resolution untouched, and introduce more convolutional layers.
?
Leave the resolution untouched, and introduce more convolutional
  layers.


This should be the next step. Two primary reasons for it :


This should reduce number of trainable parameters 
Model can learn more abstract features