Interpretable Rule Learning and Evaluation of Early Twentieth-century Music Styles

We discuss the classification of four music styles, Serialism, Impressionism, Neoclassicism, and Nationalism, of early-twentieth-century music using interpretable rule learning techniques. Three interpretable rule learning techniques are considered: decision tree, minimum description length (MDL) rule list, and rule set (the skope-rule algorithm). The features of the classifiers are fundamental musical elements based on pitch and interval distributions. Objective evaluation based on the F1 score and subjective evaluation using user study is conducted to understand the result of our classifiers from the musicians' point of view. The results show that a rule set is preferred as the algorithm attained the highest scores for objective and subjective evaluations. The rule set can also generate rules which support music theory and provide new insights regarding the musical characteristics of early twentieth-century music.