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]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}
}

Downloads: 0