  Convolutional Neural Networks have been extensively explored in the task of
automatic music tagging. The problem can be approached by using either
engineered time-frequency features or raw audio as input. Modulation filter
bank representations that have been actively researched as a basis for timbre
perception have the potential to facilitate the extraction of perceptually
salient features. We explore end-to-end learned front-ends for audio
representation learning, ModNet and SincModNet, that incorporate a temporal
modulation processing block. The structure is effectively analogous to a
modulation filter bank, where the FIR filter center frequencies are learned in
a data-driven manner. The expectation is that a perceptually motivated filter
bank can provide a useful representation for identifying music features. Our
experimental results provide a fully visualisable and interpretable front-end
temporal modulation decomposition of raw audio. We evaluate the performance of
our model against the state-of-the-art of music tagging on the MagnaTagATune
dataset. We analyse the impact on performance for particular tags when
time-frequency bands are subsampled by the modulation filters at a
progressively reduced rate. We demonstrate that modulation filtering provides
promising results for music tagging and feature representation, without using
extensive musical domain knowledge in the design of this front-end.
