Extended pipeline for content-based feature engineering in music genre recognition

TL;DR

This paper introduces an extended feature engineering pipeline for music genre recognition that combines multiple feature selection and extraction phases, improving classification performance on the GTZAN dataset.

Contribution

It proposes a novel, flexible pipeline that integrates feature selection, nonlinear correlation analysis, and cycle traversal, enhancing traditional methods for music genre classification.

Findings

Improved classification accuracy on GTZAN dataset.

Effective combination of feature selection and nonlinear correlation.

Flexible pipeline allows for better feature representation.

Abstract

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.