Tonal complexity features for style classification of classical music

We propose a set of novel audio features for classifying the style of classical music. The features rely on statistical measures based on a chroma feature representation of the audio data and describe the tonal complexity of the music, independently from the orchestration or timbre of the music. To analyze this property, we use a dataset containing piano and orchestral music from four general historical periods including Baroque, Classical, Romantic, and Modern. By applying dimensionality reduction techniques, we derive visualizations that demonstrate the discriminative power of the features with regard to the music styles. In classification experiments, we evaluate the features' performance using an SVM classifier. We investigate the influence of artist filtering with respect to the individual composers on the classification performance. In all experiments, we compare the results to the performance of standard features. We show that the introduced features capture meaningful properties of musical style and are robust to timbral variations.