I am computing a linear spectrogram of an audio signal.
The spectrogram is a 2-D matrix with time on the x-axis and frequency on the y-axis. The traditional approach is to apply a color mapping to convert the spectrogram (with values from -80 to 0) to an image with three color channels. This effectively makes it a 3-D tensor. Then we feed that into the convolutional neural net.
Does this color mapping step really need to be done? The color mapping will have different nonzero ranges on different channels, but what are we gaining? The color map is just derived from the 2-D matrix so I don't see how there is any information increase, but certainly it adds to the computational complexity. And it seems like it might really depend on which color map is used, adding another layer of complexity.
Do I really need three channels since all of the information is in the 2-D matrix? How does this affect the ability of the CNN to detect certain audio markers?