A Music Classification Approach Based on the Trajectory of Fifths

In this paper we examine the applicability of the trajectory of fifths as a source of knowledge in automated music classification processes. The study shows that such trajectories provide valuable information concerning the harmonic structure of a given piece of music. The results of the conducted experiments indicate that even basic coefficients quantifying the trajectory of fifths (e.g. the length of the trajectory) can be utilized as feature variables in music classification algorithms. The performed experiments involved the computation of the trajectories of fifths for the music pieces from two genre groups - rock/pop and jazz, calculation of their basic coefficients, and using these coefficients as feature variables for various rock/pop - jazz classifiers based on popular machine learning algorithms, i.e. Logistic Regression, Decision Tree, Random Forest, K-Nearest Neighbors, Linear Discriminant Analysis, Gaussian Naïve Bayes, and C-SVM. The tests evaluating the performance of the established classifiers have shown that in all of the considered cases the mean balanced accuracies were greater than 0.900, reaching the maximum of 0.936 (std=0.019) for the C-SVM classifier and the minimum of 0.910 (std=0.017) for the classification using the Decision Tree algorithm. Given the basic nature of the utilized coefficients and the relative simplicity of the applied classification models, the achieved results seem very promising. They strongly encourage further research concerning the use of the trajectory of fifths in different music classification algorithms – especially evaluation of its potential in the multi-class or multi-label genre classification scenarios.