  We developed task-optimized deep neural networks (DNNs) that achieved
state-of-the-art performance in different evaluation scenarios for automatic
music tagging. These DNNs were subsequently used to probe the neural
representations of music. Representational similarity analysis revealed the
existence of a representational gradient across the superior temporal gyrus
(STG). Anterior STG was shown to be more sensitive to low-level stimulus
features encoded in shallow DNN layers whereas posterior STG was shown to be
more sensitive to high-level stimulus features encoded in deep DNN layers.
