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

Year 2024, , 28 - 54, 30.06.2024

Frank Scherbaum , Simha Arom , Florent Caron Darras , Ana Lolashvili , Frank Kane

https://doi.org/10.33906/musicologist.1246886

Abstract

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.

Keywords

Traditional Georgian Vocal Music, Classification, Computational Ethnomusicology

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