I am aiming to conduct a classification-based study using signals collected from various devices. I've researched other approaches which make use of STFT for producing a spectrogram for speech and EEG/ECG signals and classifying the grayscale images.
While this is potentially a preferred approach, some speech-based classifications would make use of MFCC's, for example, from which some
n mfcc coefficients would be used per sample as feature columns.
My question here is, in order to potentially speed up my model for a variety of classes and large number of samples - is there a preferred approach to avoid images and instead represent STFT (and/or other features) for signal sample classification?