An alphabet-reduction algorithm for chordal n-grams

Abstract

Computational music theorists have long been concerned with ways to parse musical surfaces into workable chords that conform to music-theoretical intuitions. This study proposes an algorithm that groups surface structures into relational networks that balance a chord's contextual position and its scale-degree content. Applying the algorithm to a corpus of thousands of MIDI files that stretch throughout the common practice successfully derives an intuitive chord alphabet. The study raises issues concerning traditional harmonic-function theory, suggests a potential model of listeners' learning of tonality's basic cognitive elements, and proposes to a method of reducing surface complexity in corpus studies. © 2013 Springer-Verlag.