Investigating style evolution of Western classical music: A computational approach

Abstract

In musicology, there has been a long debate about a meaningful partitioning and description of music history regarding composition styles. Particularly, concepts of historical periods have been criticized since they cannot account for the continuous and interwoven evolution of style. To systematically study this evolution, large corpora are necessary suggesting the use of computational strategies. This article presents such strategies and experiments relying on a dataset of 2000 audio recordings, which cover more than 300 years of music history. From the recordings, we extract different tonal features. We propose a method to visualize these features over the course of history using evolution curves. With the curves, we re-trace hypotheses concerning the evolution of chord transitions, intervals, and tonal complexity. Furthermore, we perform unsupervised clustering of recordings across composition years, individual pieces, and composers. In these studies, we found independent evidence of historical periods that broadly agrees with traditional views as well as recent data-driven experiments. This shows that computational experiments can provide novel insights into the evolution of styles.

The Impact of Voice Leading and Harmony on Musical Expectancy

“…For the domain of harmony, the key criterion is root motion 7,15 . In common-practice tonality, there is a strong preference for root motion to be in descending fifths (this also applies to the Jazz idiom 16 ), followed by descending thirds and ascending seconds; root motions inverting these interval directions (ascending fifth, ascending third, and descending second) are less prominent [17][18][19] . This distribution sets common-practice tonality apart from Pop/ Rock tonality, which is characterised by almost a reversal of these frequencies 20 .…”

Statistical Evolutionary Laws in Music Styles

“…In this study, we consider the evolution of musical styles, which has gathered growing attention [3,4,[9][10][11][12]. It has been observed in a recent paper [5] that some features of music, e.g. the frequency of tritones 1 , have steadily increased during the history of Western classical music.…”

Visualizing music similarity: clustering and mapping 500 classical music composers

“…The formula is: CSCi, j = (ad − bc)/sqrt [(a + b) where a, b, c, d are the count of composers in sets described in Footnote 4. Weiss et al 2018) typically show that composers tend to cluster in ways that conform to our intuitions about stylistic traditions. In this paper we will compare the clustering performance of our context-based approach versus the content-based approach of Weiss (2017).…”

“…Musicologists have typically recounted the history of classical music by classifying composers into defined groups such as Renaissance, Baroque, Classical, Romantic, and Modern periods. See for example Taruskin (2010), and Taruskin and Gibbs (2013), henceforth T&G. Recently, clustering composers using statistical analyses of content-based information such as audio recordings in the case of Weiss (2017) and Weiss et al (2018), have generated results that, on average, tend to conform to traditional musicology analyses. For example, Weiss (2017) selects commercial audio recordings of 1600 pieces (movements) for 70 composers and averages audio features (such as chord progressions, interval classes, and complexity features) of the recordings over each composer.…”

“…The unit of study is typically the 'composition' and the analysis is to compare similarities/differences across audio signals from a series of compositions or from different segments of a same composition (self-similarity). This particular angle has been used among others, by Foote (1999), Pampalk (2006), and more recently by Weiss (2017) and Weiss et al (2018) for classical music, and by Mauch et al (2015) for popular music where they investigate the evolution of musical diversity and disparity and whether evolution has been gradual or punctuated. Context-based MIR is motivated by the fact that there are aspects not encoded in an audio signal or that cannot be extracted from it, but which are nevertheless important to human perception of music, for example, the cultural background of a composer.…”