TY - GEN
T1 - Modeling harmony with skip-grams
AU - Sears, David R.W.
AU - Arzt, Andreas
AU - Frostel, Harald
AU - Sonnleitner, Reinhard
AU - Widmer, Gerhard
N1 - Publisher Copyright:
© 2019 Sears, Arzt, Frostel, Sonnleitner, Widmer.
PY - 2017
Y1 - 2017
N2 - 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).
AB - 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).
UR - http://www.scopus.com/inward/record.url?scp=85069869482&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85069869482
T3 - Proceedings of the 18th International Society for Music Information Retrieval Conference, ISMIR 2017
SP - 332
EP - 338
BT - Proceedings of the 18th International Society for Music Information Retrieval Conference, ISMIR 2017
A2 - Cunningham, Sally Jo
A2 - Duan, Zhiyao
A2 - Hu, Xiao
A2 - Turnbull, Douglas
PB - International Society for Music Information Retrieval
T2 - 18th International Society for Music Information Retrieval Conference, ISMIR 2017
Y2 - 23 October 2017 through 27 October 2017
ER -