Comparing Human and Computational Models of Music Prediction

Witten, Ian H.; Manzara, Leonard C.; Conklin, Darrell

doi:10.2307/3680523

Cited by 23 publications

(23 citation statements)

References 11 publications

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“…Profiles for both model entropy and human entropy are shown in Figures 6 and 7 for Chorales 61 and 151 respectively. The entropy profiles illustrate the close correspondence between model entropy and human entropy throughout each of the chorale melodies (see also Witten et al, 1994). The statistical model provided a closer fit to the data than the two-factor model, which accounted for approximately 13% of the variance in the data, R ϭ .41, R 2 adj ϭ .13, F(3,78) ϭ 5.17, p Ͻ .01, and this difference was found to be significant, t(79) ϭ 5.15, p Ͻ .01.…”

Section: Resultsmentioning

confidence: 69%

“…For both pieces, the results demonstrated a rise in uncertainty near the end of the piece before a steep decline to the final cadence. Witten, Manzara, and Conklin (1994) found a striking similarity between the human entropy profiles and those generated by a statistical model derived from 95 chorale melodies (Conklin & Witten, 1995), suggesting that the relative degrees of expectancy elicited by events throughout the pieces were similar for both the participants and the model. The experimental procedure used by Manzara et al (1992) differs from that used by Cuddy and Lunny (1995) and Schellenberg (1996) as does the nature of the data collected.…”

Section: Stage Feature Addedmentioning

confidence: 77%

See 1 more Smart Citation

Expectation in Melody: The Influence of Context and Learning

Pearce¹,

Wiggins²

2006

Music Perception

242

227

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JSTOR is a not-for-profit service that helps scholars, researchers, and students discover, use, and build upon a wide range of content in a trusted digital archive. We use information technology and tools to increase productivity and facilitate new forms of scholarship. For more information about JSTOR, please contact support@jstor.org.. Krumhansl, 1995aKrumhansl, , 1995bSchellenberg, 1996Schellenberg, , 1997. However, there is also evidence that the implemented bottom-up rules constitute too inflexible a model to account for the influence of the musical experience of the listener and the melodic context in which expectations are elicited. A theory is presented, according to which both bottom-up and top-down descriptions of observed patterns of melodic expectation may be accounted for in terms of the induction of statistical regularities in existing musical repertoires. A computational model that embodies this theory is developed and used to reanalyze existing experimental data on melodic expectancy. The results of three experiments with increasingly complex melodic stimuli demonstrate that this model is capable of accounting for listeners' expectations as well as or better than the two-factor model of Schellenberg (1997). University of California Press

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Section: Resultsmentioning

confidence: 69%

Section: Stage Feature Addedmentioning

confidence: 77%

Expectation in Melody: The Influence of Context and Learning

Pearce¹,

Wiggins²

2006

Music Perception

242

227

View full text Add to dashboard Cite

show abstract

“…In a behavioural setting, a series of probe-tone studies as well as the work by Schmuckler (1989), Manzara et al (1992) and Witten et al (1994) provided major results regarding musical feature prediction. Tone profiles (unigram distributions) as modelled by early computational approaches (e.g.…”

Section: Behavioural Findingsmentioning

confidence: 98%

Predictive information processing in music cognition. A critical review

Rohrmeier

Koelsch

2012

International Journal of Psychophysiology

164

162

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“…Information contour is a measure that describes the movement of information between two successive events (up indicating less expected than the previous event, down indicating more expected than the previous event). It can be seen as the contour of the information flow, which has been used by Witten, Manzara, and Conklin (1994) and Potter, Wiggins, and Pearce (2007) to measure information dynamics in a musical analysis. In order to measure this a viewpoint is created that expresses if the information content, with respect to a model of the corpus that does not include the template piece for each event, is higher, lower, or equal to that of the previous event.…”

Section: Information Contour (I)mentioning

confidence: 99%

Generating structured music for bagana using quality metrics based on Markov models

Herremans

Weisser

Sörensen

et al. 2015

Expert Systems with Applications

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a b s t r a c tIn this research, a system is built that generates bagana music, a traditional lyre from Ethiopia, based on a first order Markov model. Due to the size of many datasets it is often only possible to get rich and reliable statistics for low order models, yet these do not handle structure very well and their output is often very repetitive. A first contribution of this paper is to propose a method that allows the enforcement of structure and repetition within music, thus handling long term coherence with a first order model. The second goal of this research is to explain and propose different ways in which low order Markov models can be used to build quality assessment metrics for an optimization algorithm. These are then implemented in a variable neighborhood search algorithm that generates bagana music. The results are examined and thoroughly evaluated.

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Comparing Human and Computational Models of Music Prediction

Cited by 23 publications

References 11 publications

Expectation in Melody: The Influence of Context and Learning

Expectation in Melody: The Influence of Context and Learning

Predictive information processing in music cognition. A critical review

Generating structured music for bagana using quality metrics based on Markov models

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