How Has Pop Music Changed Over Time

2. Introduction

The history of popular music has long been debated by philosophers, sociologists, journalists, bloggers and pop stars [i–vii]. Their accounts, though rich in brilliant musical lore and artful judgements, lack what scientists want: rigorous tests of clear hypotheses based on quantitative information and statistics. Economics-minded social scientists studying the history of music accept done better, but they are less interested in music than the means by which it is marketed [8–fifteen]. The contrast with evolutionary biology—a historical scientific discipline rich in quantitative data and models—is striking, the more so because cultural and organismic variety are both considered to be the result of modification-by-descent processes [16–19]. Indeed, linguists and archaeologists, studying the evolution of languages and material culture, usually apply the aforementioned tools that evolutionary biologists do when studying the evolution of species [20–25].

Until recently, the single greatest impediment to a scientific account of musical history has been a want of data. That has changed with the emergence of big, digitized, collections of audio recordings, musical scores and lyrics. Quantitative studies of musical development have chop-chop followed [26–30]. Here, we use a corpus of digitized music to investigate the history of American popular music. Drawing inspiration from studies of organic and cultural evolution, nosotros view the history of pop music as a 'fossil record' and ask the kinds of questions that a palaeontologist might: has the diversity of popular music increased or decreased over time? Is evolutionary change in popular music continuous or discontinuous? And, if it is discontinuous, when did the discontinuities occur?

To circumscribe our sample, we focused on songs that appeared in the US Billboard Hot 100 between 1960 and 2010. We obtained thirty-due south-long segments of 17 094 songs covering 86% of the Hot 100, with a pocket-size bias towards missing songs in the earlier years. Because our aim is to investigate the development of popular taste, we did not attempt to obtain a representative sample of all the songs that were released in the United states in that menstruum of time, but simply those that were most commercially successful.

Like previous studies of pop-music history [28,30], our study is based on features extracted from audio rather than from scores. Even so, where these early on studies focused on technical aspects of audio such as loudness, vocabulary statistics and sequential complexity, nosotros take attempted to identify musically meaningful features. To this end, we adopted an arroyo inspired by recent advances in text-mining (figure 1). We began by measuring our songs for a series of quantitative sound features, 12 descriptors of tonal content and fourteen of timbre (electronic supplementary material, M2–3). These were then discretized into 'words' resulting in a harmonic lexicon (H-dictionary) of chord changes, and a timbral lexicon (T-lexicon) of timbre clusters (electronic supplementary textile, M4). To relate the T-dictionary to semantic labels in patently English, nosotros carried out skilful annotations (electronic supplementary fabric, M5). The musical words from both lexica were and then combined into eight+eight=16 'topics' using latent Dirichlet allocation (LDA). LDA is a hierarchical generative model of a text-similar corpus, in which every document (hither: song) is represented as a distribution over a number of topics, and every topic is represented as a distribution over all possible words (here: chord changes from the H-lexicon, and timbre clusters from the T-lexicon). We obtain the most probable model past ways of probabilistic inference (electronic supplementary material, M6). Each vocal, then, is represented as a distribution over eight harmonic topics (H-topics) that capture classes of chord changes (e.g. 'ascendant-seventh chord changes') and eight timbral topics (T-topics) that capture particular timbres (due east.grand. 'drums, aggressive, percussive', 'female vox, melodic, vocal', derived from the expert annotations), with topic proportions q. These topic frequencies were the basis of our analyses.

Figure 1. Data processing pipeline illustrated with a segment of Queen's *Maverick Rhapsody*, 1975, one of the few Hot 100 hits to feature an astrophysicist on atomic number 82 guitar.

three. Results

3.1 The evolution of topics

Betwixt 1960 and 2010, the frequencies of the topics in the Hot 100 varied greatly: some topics became rarer, others became more common, even so others cycled (figure 2). To help united states interpret these dynamics, we fabricated utilise of associations betwixt the topics and particular artists also as genre-tags assigned past the listeners of Last.fm, a web-based music discovery service with approximately 50 one thousand thousand users (electronic supplementary textile, M8). Considering the H-topics first, the nearly frequent was H8 (mean ± 95% CI: $\bar{q} = 0.236 \pm 0.003$ )—major chords without changes. About two-thirds of our songs prove a substantial (>12.5%) frequency of this topic, especially those tagged every bit classic country, classic rock and love (online tables). Its presence in the Hot 100 was quite constant, beingness the about common H-topic in 43 of 50 years.

Figure 2. Development of musical topics in the Billboard Hot 100. Mean topic frequencies ( $\bar{q}$ ) ±95% CI estimated past bootstrapping.

Other H-topics were much more dynamic. Between 1960 and 2009, the mean frequency of H1 declined by about 75%. H1 captures the apply of dominant-seventh chords. Inherently dissonant (because of the tritone interval betwixt the third and the minor-seventh), these chords are normally used in Jazz to create tensions that are eventually resolved to consonant chords; in Blues music, the dissonances are typically not resolved and thus add to the characteristic 'muddy' color. Accordingly, we find that songs tagged blues or jazz take a high frequency of H1; it is especially common in the songs of Blues artists such every bit B.B. King and Jazz artists such equally Nat 'King' Cole. The refuse of this topic, so, represents the lingering death of Jazz and Blues in the Hot 100.

The remaining H-topics capture the development of other musical styles. H3, for case, embraces minor-seventh chords used for harmonic colour in funk, disco and soul—this topic is over-represented in funk and disco, and artists such equally Chic and KC & The Sunshine Band. Between 1967 and 1977, the mean frequency of H3 more than doubles. H6 combines several chord changes that are a mainstay in modal rock tunes and therefore mutual in artists with large-stadium ambitions (eastward.g. Mötley Crüe, Van Halen, REO Speedwagon, Queen, Kiss and Alice Cooper). Its increase between 1978 and 1985, and subsequent decline in the early 1990s, marks the age of Loonshit Stone. Of all H-topics, H5 shows the most striking change in frequency. This topic, which captures the absence of identifiable chord structure, barely features in the 1960s and 1970s when, a few spoken-word-music collages aside (e.yard. those of Dickie Goodman), nigh all songs had clearly identifiable chords. H5 starts to become more frequent in the belatedly 1980s and then rises apace to a elevation in 1993. This represents the rise of Hip Hop, Rap and related genres, every bit exemplified by the music of Busta Rhymes, Nas and Snoop Dog, who all use chords particularly rarely (online tables).

The frequencies of the timbral Topics, too, evolve over time. T3, described as 'energetic, spoken language, brilliant', shows the same dynamics as H5 and is too associated with the rise of Hip Hop-related genres. Several of the other timbral topics, however, appear to rise and autumn repeatedly, suggesting recurring fashions in instrumentation. For example, the evolution of T4 ('piano, orchestra, harmonic') appears sinusoidal, suggesting a return in the 2000s to timbral qualities prominent in the 1970s. T5 ('guitar, loud, energetic') underwent two total cycles with peaks in 1966 and 1985, heading up in one case more in 2009. The second, larger, superlative coincides with a acme in H6, the chord changes likewise associated with stadium rock groups such equally Mötley Crüe (online tables). Finally, T1 ('drums, ambitious, percussive') rises continuously until 1990, which coincides with the spread of new percussive technology such every bit drum machines and the gated reverb effect famously used by Phil Collins on In the air this night, 1981. Appropriately, T1 is over-represented in songs tagged trip the light fantastic, disco and new moving ridge and artists such as The Pet Shop Boys. After 1990, the frequency of T1 declines: the reign of the drum machine was over.

iii.2 The varieties of music

To analyse the evolution of musical variety, we began by classifying our songs. Popular music is classified into genres such every bit country, rock and scroll, rhythm and dejection (R`n'B) as well every bit a multitude of subgenres (trip the light fantastic toe-pop, synthpop, heartland stone, roots rock, etc.). Such genres are, nonetheless, but imperfect reflections of musical qualities. Pop music genres such equally country and rap partially capture musical styles but, besides being informal, are also based on non-musical factors such as the age or ethnicity of performers (e.g. classic rock and K[orean]-Popular) [5]. For this reason, nosotros synthetic a taxonomy of 13 styles past thou-means clustering on main components derived from our topic frequencies (figure 3 and electronic supplementary material, M9). We investigated all k<25 and found that the best clustering solution, as determined by mean silhouette score, was k=13.

In gild to relate Last.fm tags to the style clusters, we used a technique called enrichment analysis from bioinformatics. This technique is usually applied to go far at biological interpretations of sets of genes, i.eastward. to observe out what the 'part' of a prepare of genes is. Applying the GeneMerge enrichment-detection algorithm [31] to our style data, we found that all styles are strongly enriched for particular tags, i.east. for each style some Concluding.fm tags are significantly over-represented (electronic supplementary material, table S1), so nosotros conclude that they capture at least some of the structure of popular music perceived by consumers. The evolutionary dynamics of our styles reflect well-known trends in popular music. For example, the frequency of mode iv, strongly enriched for jazz, funk, soul and related tags, declines steadily from 1960 onwards. By dissimilarity, styles 5 and 13, strongly enriched for rock-related tags, fluctuate in frequency, whereas style 2, enriched for rap-related tags, is very rare earlier the mid-1980s just and then speedily expands to become the unmarried largest way for the adjacent thirty years, contracting once more in the late 2000s.

What do our styles represent? Figure 3 shows that styles and their development relate to detached subgroups of the charts (genres), and hierarchical cluster assay suggests that styles tin be grouped into a college bureaucracy. However, nosotros suppose that, unlike organisms of dissimilar biological species, all the songs in the charts comprise one large, highly structured, metapopulation of songs linked by a network of ancestor–descendant relationships arising from songwriters imitating their predecessors [32]. Styles and genres, so, correspond populations of music that accept evolved unique characters (topics), or combinations of characters, in partial geographical or cultural isolation, e.g. land in the Southern United states during the 1920s or rap in the S Bronx of the 1970s. These styles rise and fall in frequency over time in response to the changing tastes of songwriters, musicians and producers, who are in turn influenced by the audience.

three.3 Musical diversity has not declined

Just as palaeontologists have discussed the tempo and fashion of evolutionary modify in the fossil record [33], historians of music take discussed musical change and the processes that drive it. Some have argued that oligopoly in the media industries has caused a relentless decline in cultural variety of new music [i,ii], whereas others suggest that such homogenizing trends are periodically interrupted past small competitors offering novel and varied content resulting in 'cycles of symbol production' [8,12]. For want of data, there have been few tests of either theory [nine–11,fourteen].

To test these ideas, we estimated iv yearly measures of diverseness (effigy 4). We plant that although all four evolve, two—topic diversity and disparity—show the most striking changes, both failing to a minimum around 1986, but then rebounding and increasing to a maximum in the early 2000s. Because neither of these measures runway song number, their dynamics cannot be due to varying numbers of songs in the Hot 100; nor, because our sampling over 50 years is nigh complete, tin can they be due to the over-representation of recent songs—the so-called pull of the recent [34]. Instead, their dynamics are due to changes in the frequencies of musical styles.

Figure 4. Evolution of musical diverseness in the Billboard Hot 100. Nosotros estimate four measures of diversity. From left to right: song number in the charts, D _N, depends simply on the rate of turnover of unique entities (songs), and takes no business relationship of their phenotypic similarity. Form diversity, D _S, is the effective number of styles and captures functional variety. Topic variety, D _T, is the constructive number of musical topics used each yr, averaged across the harmonic and timbral topics. Disparity, D _Y, or phenotypic range is estimated as the full standard deviation within a year. Annotation that although in ecology D _South and D _Y are often applied to sets of distinct species or lineages they demand not be; our utilise of them implies nothing nigh the ontological status of our styles and topics. For total definitions of the variety measures, come across electronic supplementary material, M11. Shaded regions define eras separated past musical revolutions (figure 5).

The decline in topic diversity and disparity in the early 1980s is due to a pass up of timbral rather than harmonic diversity (electronic supplementary material, effigy S1). This tin be seen in the evolution of particular topics (figure 2). In the early on 1980s timbral topics T1 (drums, aggressive, percussive) and T5 (guitar, loud, energetic) become increasingly ascendant; the subsequent recovery of diversity is due to the relative decrease in frequency of the these topics as T3 (energetic, oral communication, bright) increases. Put in terms of styles, the refuse of diversity is due to the authority of genres such equally new wave, disco, hardrock; its recovery is due to their waning with the rise of rap and related genres (effigy 2). Contrary to current theories of musical evolution, so, we find no show for the progressive homogenization of music in the charts and niggling sign of multifariousness cycles within the l year time frame of our written report. Instead, the evolution of nautical chart diversity is dominated by historically unique events: the ascension and fall of particular means of making music.

three.four Musical evolution is punctuated by revolutions

The history of popular music is oft seen as a succession of distinct eras, east.g. the 'Rock Era', separated by revolutions [3,6,14]. Against this, some scholars accept argued that musical eras and revolutions are illusory [5]. Even amid those who see discontinuities, there is little agreement almost when they occurred. The problem, again, is that data accept been scarce, and objective criteria for deciding what constitutes a break in a historical sequence scarcer yet.

To determine direct whether rate discontinuities exist we divided the menstruation 1960–2010 into 200 quarters and used the primary components of the topic frequencies to estimate a pairwise distance matrix between them (figure 5a). This matrix suggested that, while musical evolution was ceaseless, there were periods of relative stasis punctuated by periods of rapid change. To test this impression, we applied a method from Music Data Retrieval, Foote Novelty, which estimates the magnitude of alter in a altitude matrix over a given temporal window [35]. The method relies on a kernel matrix with a checkerboard design. Because a distance matrix exposes only such a checkerboard blueprint at change points [35], convolving information technology with the checkerboard kernel along its diagonal directly yields the novelty function (electronic supplementary material, M11). We calculated Foote Novelty for all windows between ane and 10 years and, for each window, determined empirical significance cut-offs based on random permutation of the distance matrix. We identified three revolutions: a major one around 1991 and ii smaller ones effectually 1964 and 1983 (effigy 5b). From peak to succeeding trough, the charge per unit of musical modify during these revolutions varied four- to half-dozen-fold.

Figure 5. — Figure v. Musical revolutions in the Billboard Hot 100. (a) Quarterly pairwise distance matrix of all the songs in the Hot 100. (b) Rate of stylistic change based on Foote Novelty over successive quarters for all windows ane–ten years, inclusive. The charge per unit of musical alter—deadening-to-fast—is represented by the colour slope blue, green, yellow, red, brown: 1964, 1983 and 1991 are periods of peculiarly rapid musical alter. Using a Foote Novelty kernel with a one-half-width of 3 years results in significant change in these periods, with Novelty peaks in 1963–Q4 (p<0.01), 1982–Q4 (p<0.01) and 1991–Q1 (p<0.001) marked by dashed lines. Significance cut-offs for all windows were empirically determined by random permutation of the distance matrix. Significance contour lines with p-values are shown in blackness.

This temporal analysis, when combined with our fashion clusters (figure 3), shows how musical revolutions are associated with the expansion and wrinkle of particular musical styles. Using quadratic regression models, we identified the styles that showed pregnant (p<0.01) alter in frequency against time in the 6 years surrounding each revolution (electronic supplementary material, tabular array S2). We besides carried out a style-enrichment analysis for the same periods (electronic supplementary cloth, tabular array S2). Of the iii revolutions, 1964 was the most complex, involving the expansion of several styles—1, 5, 8, 12 and 13—enriched at the fourth dimension for soul- and rock-related tags. These gains were bought at the expense of styles 3 and 6, both enriched for doowop among other tags. The 1983 revolution is associated with an expansion of iii styles—eight, 11 and 13—here enriched for new moving ridge-, disco- and hard rock-related tags and the wrinkle of three styles—iii, 7 and 12—hither enriched for soft stone-, country-related or soul + r`n'b-related tags. The largest revolution of the iii, 1991, is associated with the expansion of way 2, enriched for rap-related tags, at the expense of styles 5 and 13, hither enriched for rock-related tags. The rise of rap and related genres appears, then, to be the single most important event that has shaped the musical structure of the American charts in the period that we studied.

three.v The British did not start the American revolution of 1964

Our analysis does not reveal the origins of musical styles; rather, it shows when changes in style frequency affect the musical structure of the charts. Begetting this in mind, we investigated the roles of particular artists in one revolution. On 26 December 1963, The Beatles released I want to hold your manus in the Usa. They were swiftly followed past dozens of British acts who, over the side by side few years, flooded the American charts. It is often claimed that this 'British Invasion' (BI) was responsible for musical changes of the time [36]. Was it? Equally noted above, effectually 1964, many styles were changing in frequency; many chief components of the topic frequencies show linear changes in this period too. Inspection of the get-go 4 PCs shows that their evolutionary trajectories were all established before 1964, implying that, whereas the British may have contributed to this revolution, they could non have been entirely responsible for it (figure 6a). We then compared ii of the nigh successful BI acts, The Beatles and The Rolling Stones, with the residue of the Hot 100 (figure vib). In the case of PC1 and PC2, the songs of both bands have (low) values that anticipate the Hot 100's trajectory: for these musical attributes, they were literally alee of the curve. In the instance of PC3 and PC4, their songs resemble the rest of the Hot 100: for these musical attributes, they were simply on-trend. Together, these results advise that, even if the British did not initiate the American revolution of 1964, they did exploit it and, to the degree that they were imitated by other artists, fanned its flames. Indeed, the extraordinary success of these two groups—66 Hot 100 hits between them prior to 1968—may be attributable to their having washed so.

Figure 6. — Figure half-dozen. The British Invasion in the American revolution of 1964. Top to bottom: PC1–PC4. (a) Linear evolution of quarterly medians of iv PCs in the 6 years (24 quarters) flanking 1963–Q4, the pinnacle of the 1964 revolution. The population medians of all four PCs decrease, and these decreases begin well before the start of the British Invasion (BI) in late 1963, implying that BI acts cannot be solely responsible for the changes in musical style evident at the fourth dimension. For each PC, the ii topics that load most strongly are indicated, with sign of correlation—high, red to low, blue—indicated (electronic supplementary material, figure S2). (b) Frequency density distributions of iv PCs for the Beatles, The Rolling Stones and songs past all other artists around the 1964 revolution. For PC1 and PC2, but not PC3 and PC4, The Beatles and The Rolling Stones have significantly lower median values than the rest of the population, indicated by arrows, implying that these BI artists adopted a musical fashion that exaggerated existing trends in the Hot 100 towards increased apply of major chords and decreased use of 'bright' speech (PC1) and increased guitar-driven aggression and decreased use of mellow vocals (PC2). Vertical lines represent medians; p-values based on Isle of mann–Whitney–Wilcoxon rank sum test; The Beatles (B): n=46; The Rolling Stones (RS): n=twenty; other artists (O): n=3114.

4. Discussion and Decision

Our findings provide a quantitative picture of the development of pop music in the U.s. over the course of 50 years. As such, they form the basis for the scientific study of musical change. Those who wish to make claims about how and when popular music changed can no longer appeal to anecdote, connoisseurship and theory unadorned by data. Similarly, contempo work has shown that information technology is possible to identify discrete stylistic changes in the history of Western classical music by clustering on motifs extracted from a corpus of written scores [29]. Insofar that our approach is based on audio, it tin also be practical to music for which no scores be, including that from pre-Modern cultures [19,37,38]. We accept already applied a like approach to the classification of fine art music ('classical music') into historical periods [39]. More generally, music is a natural starting betoken for the study of stylistic evolution because it is non only a universal human cultural trait [xl] just also measurable, largely determined by form, and available in a relatively standardized format (digital recordings).

Our study is limited in several ways. First, it is limited by the features studied. Our measures must capture only a fraction of the phenotypic complexity of even the simplest song; other measures may give dissimilar results. However, the finding that our classifications are supported by listener genre-tags gives u.s.a. some conviction that we have captured an important part of the perceptible variance of our sample. Second, in circumscribed our study to the Hot 100, 1960–2010, we take only sampled a small fraction of the new singles released in the USA; a complete film would require compiling a database of several 1000000 songs, which in itself is a challenge [41]. Given that the Hot 100 is certainly a biased subset of these songs, our conclusions cannot exist extended to the population of all releases. Finally, we are interested in extending the temporal range of our sample to at least the 1940s—if only to see whether 1955 was, as many have claimed, the birth date of Rock'northward'Curlicue [42].

We have not addressed the causes of the dynamics that we detect. Like whatsoever cultural artefact—and any living organism—music is the result of a variational-choice process [xvi–19]. In evolutionary biology, causal explanations of organismal diversity appeal to intrinsic constraints (developmental or genetic), ecological factors (competition amidst individuals or lineages) and stochastic events (e.g. rocks from space) [43–45]. By analogy, a causal account of the evolution of music must ultimately contain an account of how musicians imitate, and modify, existing music when creating new songs, that is, an account of the mode of inheritance, the product of musical novelty and its constraints. The showtime of these—inheritance and its constraints—is obscure [46,47]; the second—selection—less and then. The selective forces interim upon new songs are at to the lowest degree partly captured by their rise and autumn through the ranks of the charts. Many anecdotal histories of music endeavour to explain these dynamics. For instance, the rise of rap in the charts has been credited to the tv show Yo, MTV Raps! first broadcast in 1988 [48]. A general, multilevel, option theory, non restricted to Mendelian inheritance, should provide a means for such hypotheses to be tested [49–51].

Finally, we note that the statistical tools used in this study are quite full general. LDA can be used to study the evolving structure of many kinds of assemblages; Foote Novelty tin can be used to notice rate discontinuities in temporal sequences of distances based on many kinds of phenotypes. Such tools, and the existence of large digital corpora of cultural artefacts—texts, music, images, computer-aided pattern (CAD) files—at present permits the evolutionary analysis of many dimensions of modernistic civilisation. Nosotros anticipate that the study of cultural trends based upon such datasets will before long constrain and inspire theories about the evolution of civilization just as the fossil tape has for the development of life [52,53].

Data accessibility

All methods and supplementary figures and tables are bachelor in the electronic supplementary textile. Extensive data, including song titles, artists, topic frequencies and tags are available from the Figshare repository: primary data frame 10.6084/m9.figshare.1309953; secondary data frame x.6084/m9.figshare.1309950.

Funding statement

Grand.M. is supported by a Purple University of Engineering Research Fellowship.

Author contributions

M.L. provided data; 1000.One thousand., R.Thou.M. and A.1000.Fifty. analysed the data; M.M. and A.Chiliad.L. conceived the study, designed the written report, coordinated the study and wrote the manuscript. All authors gave terminal approval for publication.

Disharmonize of interests

We take no competing interests.

Acknowledgements

We thank the public participants in this written report; Austin Burt, Katy Noland and Peter Foster for comments on the manuscript; Last.fm for musical samples; Queen Mary Academy of London for the use of loftier-operation calculating facilities.

Footnotes

© 2015 The Authors. Published by the Royal Society under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/, which permits unrestricted apply, provided the original writer and source are credited.

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