An integrative approach to style analysis of folk dance melodies with classification using inductive learning

This thesis investigates the issue of the application of cognitive analysis techniques for Western art music to folk dance melodies for violin, with a view to enabling the
development of a computer tool that can aid in the identification and exploration of the stylistic characteristics of the origin of the melodies. The following questions are addressed: Can cognitive music analysis techniques for Western art music be applied successfully to folk dance melodies for violin? Is it possible to define an integrative analysis approach in this context drawing from existing approaches? To what extent can decision tree induction aid in the classification and interpretation of the analysis results? How might the musical data for analysis be represented on computer? What is the best approach to program development for an automated music analysis tool in
this context? A series of experiments using samples of American and Irish melodies are presented that verify the use, in this context, of the cognitive analysis approaches of Lerdahl and lackendoff and Narmour. Statistical approaches have also been investigated, since research has shown that such methods can reflect the way in which listeners mentally
organise the music that they hear. To enable the analysis to be carried out in an algorithmic way, an experiment using human subjects to further the work of Lerdahl and lackendoff was required. An integrative analysis approach has been identified that can be carried out in an algorithmic way therefore lending itself to future implementation on computer. In order to interpret the results of the analysis process, a decision tree induction tool (SeeS) based on Quinlan's CS algorithm was employed. SeeS was able to classify the melodies based on the attributes derived from the analysis. The decision trees and rules derived by the tool enabled the identification of features of the melodies that pertain to their origins, thus enabling a deeper understanding of the stylistic variations of the melodies.
A further experiment indicated that the cognitive analysis approaches and subsequent classification with SeeS compares favourably with the classification abilities of human subjects after a small amount of training in the musical context. Further inductive learning techniques (decision tree induction using Friedman's CART, and neural networks) have been applied to the problem of classification and interpretation of the analysis results, and although the neural network classified the musical samples with greater accuracy (illustrated using ROC analysis), decision tree
induction has been shown to be a more appropriate method in this context. Approaches to music representation and subsequent program development have been investigated, resulting in a proposal for future computer implementation of a music analysis tool using the Humdrum toolkit as a means of representation, and a declarative language for the program development.