What Can We Learn About the Grammar of Traditional Georgian Vocal Music from Computational Score Analysis?

This paper describes the current status of a long-term project aimed at understanding the chordal syntax of traditional Georgian vocal music by analyzing sheet music in Western 5-line staff notation. As an important milestone, we present a generative grammar model based on the self-learning Kohonen model (Kohonen, 1989) in a prefix tree (Antonov, 2018; 2023) framework. This represents a significant improvement over the classical Markov model, as it allows for the influence of different context lengths for each chord in a chord sequence. We used this model to generate a large number of chord sequences, all conforming to the same grammatical production rules as our corpus. These were then used as training data for an artificial neural network to test whether, as in large language models (LLMs), ‘linguistic relationships’ could be identified by visually analyzing the embedding space of the network. The results for chord-to-chord relationships are inconclusive, as the spatial structure of the embedding map for individual chords cannot be interpreted unambigously. The embedding map for whole songs, however, shows a pronounced spatial clustering which reflects the different classes of our corpus. This suggests that the structure of the embedding map reflects the similarities and dissimilarities of the chordal syntax of the individual songs, which the network has learned in an unsupervised way.