Simulating melodic and harmonic expectations for tonal cadences using probabilistic models. Sears, D. R., Pearce, M. T., Caplin, W. E., & McAdams, S. Journal of New Music Research, 47(1):29–52, Routledge, 2018. ![Simulating melodic and harmonic expectations for tonal cadences using probabilistic models [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex This study examines how the mind's predictive mechanisms contribute to the perception of cadential closure during music listening. Using the Information Dynamics of Music model (or IDyOM) to simulate the formation of schematic expectations—a finite-context (or n-gram) model that predicts the next event in a musical stimulus by acquiring knowledge through unsupervised statistical learning of sequential structure—we predict the terminal melodic and harmonic events from 245 exemplars of the five most common cadence categories from the classical style. Our findings demonstrate that (1) terminal events from cadential contexts are more predictable than those from non-cadential contexts; (2) models of cadential strength advanced in contemporary cadence typologies reflect the formation of schematic expectations; and (3) a significant decrease in predictability follows the terminal note and chord events of the cadential formula.
@Article{ sears.ea2018-simulating,
author = {Sears, David R.W. and Pearce, Marcus Thomas and Caplin,
William Earl and McAdams, Stephen},
year = {2018},
title = {Simulating melodic and harmonic expectations for tonal
cadences using probabilistic models},
abstract = {This study examines how the mind's predictive mechanisms
contribute to the perception of cadential closure during
music listening. Using the Information Dynamics of Music
model (or IDyOM) to simulate the formation of schematic
expectations—a finite-context (or n-gram) model that
predicts the next event in a musical stimulus by acquiring
knowledge through unsupervised statistical learning of
sequential structure—we predict the terminal melodic and
harmonic events from 245 exemplars of the five most common
cadence categories from the classical style. Our findings
demonstrate that (1) terminal events from cadential
contexts are more predictable than those from
non-cadential contexts; (2) models of cadential strength
advanced in contemporary cadence typologies reflect the
formation of schematic expectations; and (3) a significant
decrease in predictability follows the terminal note and
chord events of the cadential formula.},
doi = {10.1080/09298215.2017.1367010},
issn = {17445027},
journal = {Journal of New Music Research},
keywords = {Cadence,expectation,music analysis with computers,n-gram
models,segmental grouping,statistical learning},
mendeley-tags= {music analysis with computers},
number = {1},
pages = {29--52},
publisher = {Routledge},
url = {https://doi.org/10.1080/09298215.2017.1367010},
volume = {47}
}
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