Beneath (or beyond) the surface: Discovering voice-leading patterns with skip-grams

TL;DR

This paper introduces a skip-gram based computational method to detect elusive recurrent voice-leading patterns like the MRDCC in complex polyphonic music, improving pattern discovery accuracy.

Contribution

It extends the n-gram approach with skip-grams and demonstrates its effectiveness in identifying hidden voice-leading patterns in Western tonal music datasets.

Findings

MRDCC ranks higher with 5 skips in the model.

Filtering out non-harmonic change n-grams improves detection.

Statistical association measures enhance pattern ranking.

Abstract

Recurrent voice-leading patterns like the Mi-Re-Do compound cadence (MRDCC) rarely appear on the musical surface in complex polyphonic textures, so finding these patterns using computational methods remains a tremendous challenge. The present study extends the canonical n-gram approach by using skip-grams, which include sub-sequences in an n-gram list if their constituent members occur within a certain number of skips. We compiled four data sets of Western tonal music consisting of symbolic encodings of the notated score and a recorded performance, created a model pipeline for defining, counting, filtering, and ranking skip-grams, and ranked the position of the MRDCC in every possible model configuration. We found that the MRDCC receives a higher rank in the list when the pipeline employs 5 skips, filters the list by excluding n-gram types that do not reflect a genuine harmonic change between adjacent members, and ranks the remaining types using a statistical association measure.