In many applications, signals are pre-processed to yield features in a different (often frequency) domain. The underlying assumption is that subsequent classification becomes simpler and/or more robust. If the assumption holds, a less complex classifier should result from training. In this work, the effect of FFT, Wavelet and Hilbert-Huang transform (HHT) on the complexity (depth, width, parameter..) of the resulting deep model shall be explored.