A Classic Turn of Phrase: Music and the Psychology of Convention

Cavett, Esther; Gjerdingen, Robert O.

doi:10.2307/854046

Cited by 4 publications

(5 citation statements)

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“…Yet few such studies have applied the schema concept to the variety of recurrent harmonic and melodic ending formulae found in Western tonal music-what theorists and composers have for centuries called cadences. Indeed, the tonal cadence is generally considered to be the quintessential phrase-level event schema (Eberlein, 1997;Eberlein & Fricke, 1992;Gjerdingen, 1988;Meyer, 1967;Rosner & Narmour, 1992;Sears, 2015Sears, , 2016Sears, Caplin, & McAdams, 2014;Temperley, 2004), with the specificity of the mental representation reflected in the strength and specificity of the schematic expectations it generates. And yet experimental studies explicitly examining the perception of cadences (or cadential closure) are notably few (e.g., Tillmann, Bigand, & Madurell, 1998;Sears et al, 2014).…”

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confidence: 99%

Expecting the end: Continuous expectancy ratings for tonal cadences

Sears

Spitzer

Caplin

et al. 2018

Psychology of Music

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Cognitive accounts for the formation of expectations during music listening have largely centered around mental representations of scales using both melodic and harmonic stimuli. This study extends these findings to the most recurrent cadence patterns associated with tonal music using a real-time, continuous-rating paradigm. Musicians and nonmusicians heard cadential excerpts selected from Mozart’s keyboard sonatas (perfect authentic cadence [PAC], imperfect authentic cadence [IAC], half cadence [HC], deceptive cadence [DC], and evaded cadence [EV]), and continuously rated the strength of their expectations that the end of each excerpt is imminent. As predicted, expectations for closure increased over the course of each excerpt and then peaked at or near the target melodic tone and chord. Cadence categories for which tonic harmony was the expected goal (PAC, IAC, DC, EV) received the highest and earliest expectancy ratings, whereas cadence categories ending with dominant harmony (HC) received the lowest and latest ratings, suggesting that dominant harmony elicits weaker expectations in anticipation of its occurrence in cadential contexts. A regression analysis also revealed that longer excerpts featuring dense textures and a cadential six-four harmony received the highest ratings overall.

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confidence: 99%

Expecting the end: Continuous expectancy ratings for tonal cadences

Sears

Spitzer

Caplin

et al. 2018

Psychology of Music

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“…Bigand and Poulin-Charronnat ( 2006) discuss how some music-perceptual abilities result purely from exposure to (experience of) music, whereas others require explicit training. Their study does not include judgement of stylistic success, but we extrapolate to hypothesise that the less experienced our listeners, the less likely they are to notice unidiomatic chords or the absence of appropriate musical schemata (Gjerdingen, 1988(Gjerdingen, , 2007, and so the higher on average their ratings of stylistic success will be compared to more experienced listeners.…”

Section: Music Transformer Will Outperform Musicvae On Stylistic Succ...mentioning

confidence: 88%

“…8 Its outputs have been the subject of multiple, rigorously conducted listening studies (Collins et al, 2016(Collins et al, , 2017, and the starting point for use by artists in the AI Song Contest. 9 Research by Gjerdingen (1988) on the Classical style suggests excerpts up to 4 bars in length can sound stylistically coherent without structural inheritance. When structural inheritance is required by a MAIA Markov user, it is accomplished by hard-coding a repetitive structure (e.g., reuse of bars 1-4 in bars 5-8) or running a pattern discovery algorithm such as SIARCT (Collins et al, 2013(Collins et al, , 2010 to obtain one automatically.…”

Section: Sequential Modelsmentioning

confidence: 99%

Deep learning’s shallow gains: a comparative evaluation of algorithms for automatic music generation

et al. 2023

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Deep learning methods are recognised as state-of-the-art for many applications of machine learning. Recently, deep learning methods have emerged as a solution to the task of automatic music generation (AMG) using symbolic tokens in a target style, but their superiority over non-deep learning methods has not been demonstrated. Here, we conduct a listening study to comparatively evaluate several music generation systems along six musical dimensions: stylistic success, aesthetic pleasure, repetition or self-reference, melody, harmony, and rhythm. A range of models, both deep learning algorithms and other methods, are used to generate 30-s excerpts in the style of Classical string quartets and classical piano improvisations. Fifty participants with relatively high musical knowledge rate unlabelled samples of computer-generated and human-composed excerpts for the six musical dimensions. We use non-parametric Bayesian hypothesis testing to interpret the results, allowing the possibility of finding meaningful non-differences between systems’ performance. We find that the strongest deep learning method, a reimplemented version of Music Transformer, has equivalent performance to a non-deep learning method, MAIA Markov, demonstrating that to date, deep learning does not outperform other methods for AMG. We also find there still remains a significant gap between any algorithmic method and human-composed excerpts.

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“…It was important that the samples were defined in a way that instances of the schema could not be 'cherry-picked' (as recognised in Meyer 1989, pp. 57-9, andCavett-Dunsby 1990). Entire genres of composers were used to demarcate samples because they comprise predefined collections of works and movements.…”

Section: Harmonic-rhythm Ratiosmentioning

confidence: 99%

The Butterfly Schema in the Classical Instrumental Style: A Product of the Tendency for Congruence

Rawbone¹,

Jan²

2019

Music Analysis

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This article integrates theories that explain musical schemata as psychological and statistical associations of features with those that define schemata as collections of cognitively generated archetypes. It is proposed that styles contain a spectrum of features but tend towards congruence. This propensity is thought to originate in cognition and becomes manifest in and between the features of styles. A localised structure, termed the butterfly schema, is argued to be in part a product of the tendency for congruence which is incorporated into the Classical instrumental style. An analysis of the styles and schemata of the Baroque, Classical and Romantic periods explicates their distinct forms of congruent structuring. A survey of European instrumental music of circa 1750–1850 compares the quantity and type of butterfly schemata in samples from the first half of this time period with those from the second. It is found that butterfly schemata with highly congruent features are more common in the earlier, Classical‐period samples, suggesting that they are a product of the tendency for congruence in the particular form defined.

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A Classic Turn of Phrase: Music and the Psychology of Convention

Cited by 4 publications

References 0 publications

Expecting the end: Continuous expectancy ratings for tonal cadences

Expecting the end: Continuous expectancy ratings for tonal cadences

Deep learning’s shallow gains: a comparative evaluation of algorithms for automatic music generation

The Butterfly Schema in the Classical Instrumental Style: A Product of the Tendency for Congruence

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