A Comparative Analysis of Eurasian Folksong Corpora, using Self Organising Maps

Background in artificial intelligence research. The idea of analysis of large folksong corpora using mathematical tools and computer goes back to the 60’s, but the intensive research started in the 90’s, when a large scale of self-learning algorithms has been applied in music research. The application of artificial intelligence research and data mining in musicology allows us to discover hidden regularities of music and understand certain functions of human cognition. Background in ethnomusicology. To study interethnic and historical relations, Bartók and Kodály compared different layers of Hungarian folk music to those of other nations living in the neighborhood of Hungarians. Later, they extended the study on Anatolian, Mari and Chuvash folk music. The fascinating results of these latter comparisons lead to the conclusion that folk music can preserve very old common musical structures, and may refer to early cultural contacts. Aims. We report on a comparative analysis of 16 folksong corpora representing different folk music traditions in Eurasia. Our aim is to reveal some hidden musical relations between different cultures and areas of the continent. Main contribution. We applied self organizing mapping for automatic classification of melody contours of 16 European and Asian folksong corpora. We characterized the strength of the contacts between musical cultures by a probability density function. We show that the relationships identify an “Eastern” and a “Western” sub-system that are associated due to the close relations between the Finnish and Irish-Scottish-English musical cultures to the Carpathian Basin. We also show that “Eastern” cultures define a very clear overlap of melody types, functioning as a common crystallization point of musical evolution. Implications. The results show that computer-aided music analysis can reveal a complete system of cross-cultural relations, and may detect early historical contacts.