On the Classification of Traditional Georgian Vocal Music by Computer-Assisted Score Analysis

This paper describes a feasibility study for the computational classification of traditional three-voiced Georgian vocal music, based on characteristic chord sequences extracted from digital scores. We demonstrate that for this purpose the differences between Western 5-line staff notation and a more appropriate heptatonic system for traditional Georgian music can be adjusted for by a simple transformation. A corpus of about 500 digital scores, consisting of labeled ‘song classes’, i.e. subsets of folk songs from different regions and of liturgical songs in different styles, served as a testbed for the development of a classification procedure based on a higher-order Markov model that - in addition to the classification - yields chord progression sequences for each ‘song class’. Their capacity for interpretation was tested by one hundred cross-validation runs, in which randomly selected subsets of ¾ of the size of each song class were used to train classifiers, which were then applied to the remaining ¼ subsets. The sizes of the intersections of the successfully classified songs in all cross-validations are interpreted as direct measures of the degree of representativeness of the songs for their respective ‘song classes’. Based on a second validation experiment, in which we split up the datasets into equally sized subsets of ½ and ¼ of the original subsets, respectively, we estimate that the smallest subset size for an interpretation of the observed chord progression patterns as properties of a ‘song class’ is of the order of 50 songs. Currently, in our corpus, this requirement is only met by the subsets from Svaneti and Shemokmedi.