  In this paper, we empirically investigate the effect of audio preprocessing
on music tagging with deep neural networks. We perform comprehensive
experiments involving audio preprocessing using different time-frequency
representations, logarithmic magnitude compression, frequency weighting, and
scaling. We show that many commonly used input preprocessing techniques are
redundant except magnitude compression.
