I am currently doing pattern recognition on spectograms of audio files using convolutional neural networks. The spectograms are made using matplotlib cm.jet colormaps.  Problem with this color map is that it auto ranges its colors based on the min and max value of the input it is given. so an example:Spectrograms of two different version of audio file. One with static filter output, and the other with delta filter outputs. RGB values show no difference in ranges for both delta and normal, but the db, scale shows a big difference. My input consist of one column of the static and one column of delta, or a matrix (40,2,3), but since these ranges are very different kinda make me suspect that this would not work very well. Am I right or wrong?
Does the input data representation matter while training CNN for speech recognition?
visualization;data;image recognition;audio recognition
Data representation does matter because this is all the information that you pass to a learning algorithm. It is normal for static and delta (delta-delta) to have different range (I have worked with mfccs). They represent different information.Static features can be small but they may change rapidly making delta large or vice-versa. The blue regions in the first spectrogram (low magnitude) becomes red in second( high magnitude). As long as all the input are processed in the same manner (static followed by delta followed by delta-delta or any order), it won't be a problem. 