Automatic tune family identification by musical sequence alignment

Musics, like languages and genes, evolve through a process of transmission, variation, and selection. Evolution of musical tune families has been studied qualitatively for over a century, but quantitative analysis has been hampered by an inability to objectively distinguish between musical similarities that are due to chance and those that are due to descent from a common ancestor. Here we propose an automated method to identify tune families by adapting genetic sequence alignment algorithms designed for automatic identification and alignment of protein families. We tested the effectiveness of our method against a high-quality ground-truth dataset of 26 folk tunes from four diverse tune families (two English, two Japanese) that had previously been identified and aligned manually by expert musicologists. We tested different combinations of parameters related to sequence alignment and to modeling of pitch, rhythm, and text to find the combination that best matched the ground-truth classifications. The best-performing automated model correctly grouped 100% (26/26) of the tunes in terms of overall similarity to other tunes, identifying 85% (22/26) of these tunes as forming distinct tune families. The success of our approach on a diverse, cross-cultural ground-truth dataset suggests promise for future automated reconstruction of musical evolution on a wide scale.