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. 2012:2:521.
doi: 10.1038/srep00521. Epub 2012 Jul 26.

Measuring the evolution of contemporary western popular music

Joan Serrà  1 , Alvaro Corral, Marián Boguñá, Martín Haro, Josep Ll Arcos
Affiliations

Measuring the evolution of contemporary western popular music

Joan Serrà et al. Sci Rep. 2012.

Abstract

Popular music is a key cultural expression that has captured listeners' attention for ages. Many of the structural regularities underlying musical discourse are yet to be discovered and, accordingly, their historical evolution remains formally unknown. Here we unveil a number of patterns and metrics characterizing the generic usage of primary musical facets such as pitch, timbre, and loudness in contemporary western popular music. Many of these patterns and metrics have been consistently stable for a period of more than fifty years. However, we prove important changes or trends related to the restriction of pitch transitions, the homogenization of the timbral palette, and the growing loudness levels. This suggests that our perception of the new would be rooted on these changing characteristics. Hence, an old tune could perfectly sound novel and fashionable, provided that it consisted of common harmonic progressions, changed the instrumentation, and increased the average loudness.

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Figures

Figure 1
Figure 1. Method schematic summary with pitch data.
The dataset contains the beat-based music descriptions of the audio rendition of a musical piece or score (G, Em, and D7 on the top of the staff denote chords). For pitch, these descriptions reflect the harmonic content of the piece, and encapsulate all sounding notes of a given time interval into a compact representation, independently of their articulation (they consist of the 12 pitch class relative energies, where a pitch class is the set of all pitches that are a whole number of octaves apart, e.g. notes C1, C2, and C3 all collapse to pitch class C). All descriptions are encoded into music codewords, using a binary discretization in the case of pitch. Codewords are then used to perform frequency counts, and as nodes of a complex network whose links reflect transitions between subsequent codewords.
Figure 2
Figure 2. Pitch distributions and networks.
(a) Examples of the rank-frequency distribution (relative frequencies z′ such that formula image). For ease of visualization, curves are chronologically shifted by a factor of 10 in the vertical axis. Some frequent and infrequent codewords are shown. (b) Examples of the density values and their fits, taking z as the random variable. Curves are chronologically shifted by a factor of 10 in the horizontal axis. (c) Average shortest path length l versus clustering coefficient C for pitch networks (right) and their randomized versions (left). Randomized networks were obtained by swapping pairs of links chosen at random, avoiding multiple links and self-connections. Values l and C calculated without considering the 10 highest degree nodes (see SI). Arrows indicate chronology (red and blue colors indicate values for more and less recent years, respectively).
Figure 3
Figure 3. Timbre distributions.
(a) Examples of the density values and fits taking z as the random variable. (b) Fitted exponents β. (c) Spearman's rank correlation coefficients for all possible year pairs.
Figure 4
Figure 4. Loudness distributions.
(a) Examples of the density values and fits of the loudness variable x. (b) Empiric distribution medians. (c) Dynamic variability, expressed as absolute loudness differences between the first and third quartiles of x, |Q1 − Q3|.
See this image and copyright information in PMC

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