  Deep convolutional neural networks (CNNs) have been actively adopted in the
field of music information retrieval, e.g. genre classification, mood
detection, and chord recognition. However, the process of learning and
prediction is little understood, particularly when it is applied to
spectrograms. We introduce auralisation of a CNN to understand its underlying
mechanism, which is based on a deconvolution procedure introduced in [2].
Auralisation of a CNN is converting the learned convolutional features that are
obtained from deconvolution into audio signals. In the experiments and
discussions, we explain trained features of a 5-layer CNN based on the
deconvolved spectrograms and auralised signals. The pairwise correlations per
layers with varying different musical attributes are also investigated to
understand the evolution of the learnt features. It is shown that in the deep
layers, the features are learnt to capture textures, the patterns of continuous
distributions, rather than shapes of lines.
