Modeling Harmony with Skip-Grams
This addresses a data sparsity problem for music researchers modeling harmony, but it is incremental as it adapts an existing NLP method to a specific domain.
The study tackled data sparsity in modeling tonal harmony for Western classical music by applying skip-grams, resulting in reduced sparsity in n-gram distributions and significantly better performance in discovering cadences compared to contiguous n-grams.
String-based (or viewpoint) models of tonal harmony often struggle with data sparsity in pattern discovery and prediction tasks, particularly when modeling composite events like triads and seventh chords, since the number of distinct n-note combinations in polyphonic textures is potentially enormous. To address this problem, this study examines the efficacy of skip-grams in music research, an alternative viewpoint method developed in corpus linguistics and natural language processing that includes sub-sequences of n events (or n-grams) in a frequency distribution if their constituent members occur within a certain number of skips. Using a corpus consisting of four datasets of Western classical music in symbolic form, we found that including skip-grams reduces data sparsity in n-gram distributions by (1) minimizing the proportion of n-grams with negligible counts, and (2) increasing the coverage of contiguous n-grams in a test corpus. What is more, skip-grams significantly outperformed contiguous n-grams in discovering conventional closing progressions (called cadences).