Segmentation and Context of Literary and Musical Sequences

TL;DR

This paper evaluates a segmentation algorithm based on Jensen-Shannon divergence on literary and musical sequences, revealing meaningful structural divisions related to characters, setting, and tonal progressions.

Contribution

It introduces a segmentation method that effectively identifies meaningful segments in symbolic sequences of literature and music, linking them to contextual and tonal changes.

Findings

Segments correspond to character and setting changes in the play.

Segments reveal tonal domains and progressions in the musical sequence.

The algorithm successfully identifies meaningful structural divisions.

Abstract

We test a segmentation algorithm, based on the calculation of the Jensen-Shannon divergence between probability distributions, to two symbolic sequences of literary and musical origin. The first sequence represents the successive appearance of characters in a theatrical play, and the second represents the succession of tones from the twelve-tone scale in a keyboard sonata. The algorithm divides the sequences into segments of maximal compositional divergence between them. For the play, these segments are related to changes in the frequency of appearance of different characters and in the geographical setting of the action. For the sonata, the segments correspond to tonal domains and reveal in detail the characteristic tonal progression of such kind of musical composition.