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

arXiv: https://arxiv.org/abs/2006.15399 · HTML: https://mlsanity.com/paper/2006.15399/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Jun 2020.

## Built on by (most cited first, 1 of 1)

- [Cadence Detection in Symbolic Classical Music using Graph Neural Networks](https://mlsanity.com/paper/2208.14819/) (2022)

## Abstract (arXiv)

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.