2017/07/14 by David R. W. Sears, Andreas Arzt, Sears, David R. W. +7
Computer Science · Neuroscience · #FOS: Computer and information sciences #Information Retrieval (cs.IR) #Music Technology and Sound Studies #Music and Audio Processing #Neuroscience and Music Perception #Sound (cs.SD)
paper · pdf · doi:10.48550/arxiv.1707.04457
openalex publication_date 2017/07/14 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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).