  We present a feature engineering pipeline for the construction of musical
signal characteristics, to be used for the design of a supervised model for
musical genre identification. The key idea is to extend the traditional
two-step process of extraction and classification with additive stand-alone
phases which are no longer organized in a waterfall scheme. The whole system is
realized by traversing backtrack arrows and cycles between various stages. In
order to give a compact and effective representation of the features, the
standard early temporal integration is combined with other selection and
extraction phases: on the one hand, the selection of the most meaningful
characteristics based on information gain, and on the other hand, the inclusion
of the nonlinear correlation between this subset of features, determined by an
autoencoder. The results of the experiments conducted on GTZAN dataset reveal a
noticeable contribution of this methodology towards the model's performance in
classification task.
