The evolution of popular music: USA 1960–2010

Abstract

In modern societies, cultural change seems ceaseless. The flux of fashion is especially obvious for popular music. While much has been written about the origin and evolution of pop, most claims about its history are anecdotal rather than scientific in nature. To rectify this, we investigate the US Billboard Hot 100 between 1960 and 2010. Using music information retrieval and text-mining tools, we analyse the musical properties of approximately 17 000 recordings that appeared in the charts and demonstrate quantitative trends in their harmonic and timbral properties. We then use these properties to produce an audio-based classification of musical styles and study the evolution of musical diversity and disparity, testing, and rejecting, several classical theories of cultural change. Finally, we investigate whether pop musical evolution has been gradual or punctuated. We show that, although pop music has evolved continuously, it did so with particular rapidity during three stylistic ‘revolutions’ around 1964, 1983 and 1991. We conclude by discussing how our study points the way to a quantitative science of cultural change.

Multi-scale Modelling of Segmentation

“…We combined single-scale models of different time scales to build matrices in which each row included a perceptual segment boundary density curve at a given time scale, and each column included boundary density for a given time point at different time scales. This multiscale model of segmentation follows previous work on tonality (Martorell Dominguez, 2013) and musical novelty description (Kaiser & Peeters, 2013;Mauch et al, 2015). We obtained a multi-scale model for each stimulus and segmentation task; Figure 2 shows each of the four multi-scale models obtained for stimulus Morton.…”

“…One of the main findings obtained via this approach was that the estimated boundary density corresponded to boundary strength ratings, since the rated strength of a subset of indicated boundaries correlated strongly with the frequency of indications, as previously predicted by Clarke and Krumhansl (1990) and Frankland and Cohen (2004). Another approach to obtaining a representation of segmentation density would be to use multi-scale models; these have been applied for music visualization and analysis of structure (Kaiser & Peeters, 2013;Martorell Dominguez, 2013;Mauch, MacCallum, Levy, & Leroi, 2015). Multi-scale models of density offer a more comprehensive representation of hierarchical aspects of segmentation than density profiles.…”

Cultural evolution of music

“…Northeast Asia [Savage, Matsumae, et al 2015]), while others have analyzed 277 musical change using theories and methods from evolutionary biology (e.g., tracing 278 the rise and fall of Western popular [Serrà et al 2012;Mauch et al 2015] and 279 classical [Zivic, Shifres, and Cecchi 2013] …”

Time Series Analysis of Garment Distributions via Street Webcam

“…For example, the analysis of Twitter content has been used to detect large-scale mood shifts [9], as well as detecting phenomena such as flu outbreaks [8]. Major shifts in culture have also been detected in book content [11] and in musical styles [10] by datadriven approaches. However, observing collective behaviour in the physical world requires a different approach, one which can take advantage of the widespread distribution of cameras and other physical sensors within our society.…”