The history of popular music has long been debated by
philosophers, sociologists, journalists, bloggers and pop stars
[1–7]. Their accounts, though rich in vivid musical lore and
aesthetic judgements, lack what scientists want: rigorous tests
of clear hypotheses based on quantitative data and statistics.
Economics-minded social scientists studying the history of music
have done better, but they are less interested in music than
the means by which it is marketed [
We obtained 30-s-long segments of 17 094 songs covering 86% of the Hot 100, with a small bias
towards missing songs in the earlier years. – problems with database
However, where these early studies focused on technical aspects of
audio such as loudness, vocabulary statistics and sequential complexity, we have attempted to identify
musically meaningful features.
To relate the
T-lexicon to semantic labels in plain English, we carried out expert annotations (electronic supplementary
- Assignment of meaning. Coding the database.
Inherently dissonant (because of the
tritone interval between the third and the minor-seventh), these chords are commonly 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 ‘dirty’ colour. Accordingly, we find that songs
tagged BLUES or JAZZ have a high frequency of H1 – all this data crunching to tell you that that 7th chord is used in jazz
After 1990, the frequency of T1 declines: the reign of the drum machine – shows how wrongheaded conclusions can be drawn from bad data (are drum machines really not being used any more? Could we reinterpret quantized live drums as drum machines?)
Popular music is classified
into genres such asCOUNTRY
,ROCK AND ROLL
,RHYTHM AND BLUES
(R‘N’B) as well as a multitude of
subgenres (DANCEPOPSYNTHPOPHEARTLAND ROCKROOTS ROCK etc.). Such genres are, however,
but imperfect reflections of musical qualities. – misunderstands the importance of genre for the sake of creating easy database
uses last.fm for reliance on categorizing songs. Last.fm users representative? Unbiased?
The history of popular music is often seen as a succession of distinct eras, e.g. the ‘Rock Era’, separated
by revolutions [3,6,14]. Against this, some scholars have argued that musical eras and revolutions
are illusory . Even among those who see discontinuities, there is little agreement about when they
occurred. The problem, again, is that data have been scarce, and objective criteria for deciding what
constitutes a break in a historical sequence scarcer yet.
- Was the point in these debates and histories to actually answer the question definitely? This guy appears to think so. I see it more as a way to create new narratives and ways on interpreting culture?
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.
- Ways of knowing
Acknowledges two limitations 1.) classifications only based on partial song extract. They are more complex. Says they are justified by fucking last.fm data. Another algorithm at work. Strawman.
2.) Database is limited to hot 100. Just argues for more data.
Can’t explain causes. Example of MTV raps is lame. Shows problem of data capture. Rap had been around for almost a decade before becoming siginificant in the charts.
Weanticipate that the study of cultural trends based upon such datasets will soon constrain and inspire
theories about the evolution of culture just as the fossil record has for the evolution of life