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Johnson 0 #genre Thomas Johnson The Graduate Center, CUNY Society for Music Theory Annual Conference November 2, 2017 ● Arlington, VA @tgj505 ● [email protected] https://tom-johnson.net/stuff/smt2017/

#genre · “#genre.” Essentially, #genre captures the adjectival quality and in-between-ness of the seemingly-flattened stylistic world of popular music categories by exploring

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Page 1: #genre · “#genre.” Essentially, #genre captures the adjectival quality and in-between-ness of the seemingly-flattened stylistic world of popular music categories by exploring

Johnson 0

#genre

Thomas Johnson The Graduate Center, CUNY

Society for Music Theory Annual Conference

November 2, 2017 ● Arlington, VA

@tgj505 ● [email protected]

https://tom-johnson.net/stuff/smt2017/

Page 2: #genre · “#genre.” Essentially, #genre captures the adjectival quality and in-between-ness of the seemingly-flattened stylistic world of popular music categories by exploring

Thomas Johnson #genre SMT: Nov. 2, 2017

1

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Page 3: #genre · “#genre.” Essentially, #genre captures the adjectival quality and in-between-ness of the seemingly-flattened stylistic world of popular music categories by exploring

Thomas Johnson #genre SMT: Nov. 2, 2017

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Page 4: #genre · “#genre.” Essentially, #genre captures the adjectival quality and in-between-ness of the seemingly-flattened stylistic world of popular music categories by exploring

Thomas Johnson #genre SMT: Nov. 2, 2017

3

EXAMPLE 3A: Model first, second, and third clusters of related artists. (n=3)

EXAMPLE 3B: Hypothetical potential clusters with related-artist overlap.

Page 5: #genre · “#genre.” Essentially, #genre captures the adjectival quality and in-between-ness of the seemingly-flattened stylistic world of popular music categories by exploring

Thomas Johnson #genre SMT: Nov. 2, 2017

4

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Page 6: #genre · “#genre.” Essentially, #genre captures the adjectival quality and in-between-ness of the seemingly-flattened stylistic world of popular music categories by exploring

Thomas Johnson #genre SMT: Nov. 2, 2017

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Page 7: #genre · “#genre.” Essentially, #genre captures the adjectival quality and in-between-ness of the seemingly-flattened stylistic world of popular music categories by exploring

Thomas Johnson #genre SMT: Nov. 2, 2017

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Page 8: #genre · “#genre.” Essentially, #genre captures the adjectival quality and in-between-ness of the seemingly-flattened stylistic world of popular music categories by exploring

Thomas Johnson #genre SMT: Nov. 2, 2017

7

EXAMPLE 7: A map of 1005 genres that Spotify uses to describe 11553

artists in my corpus. Each node is a genre; nodes are connected if they are

both used to describe a single artist. Colors present automatically generated

communities based on network properties.

EDM

Hip hop & pop Jazz

Latin

Brazilian

Classical & ambient

Indie pop & rock

Punk & Metal

Reggae & dancehall

Page 9: #genre · “#genre.” Essentially, #genre captures the adjectival quality and in-between-ness of the seemingly-flattened stylistic world of popular music categories by exploring

Thomas Johnson #genre SMT: Nov. 2, 2017

8

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Page 10: #genre · “#genre.” Essentially, #genre captures the adjectival quality and in-between-ness of the seemingly-flattened stylistic world of popular music categories by exploring

Thomas Johnson #genre SMT: Nov. 2, 2017

9

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Page 11: #genre · “#genre.” Essentially, #genre captures the adjectival quality and in-between-ness of the seemingly-flattened stylistic world of popular music categories by exploring

Thomas Johnson #genre SMT: Nov. 2, 2017

10

Selected Bibliography

Born, Georgina. 2014. “Time, the Social, the Material: For a Non-

Teleological Analysis of Musical Genre.” In “Music and Genre:

New Directions.” McGill University, Montreal.

———. 2011. “Music and the Materialization of Identities.”

Journal of Material Culture 16 (4):376–88.

Eco, Umberto. 1976. A Theory of Semiotics. Bloomington: Indiana

University Press.

Gjerdingen, Robert O., and David Perrott. 2008. “Scanning the

Dial: The Rapid Recognition of Music Genres.” Journal of New

Music Research 37 (2):93–100.

Hagstrom Miller, Karl. 2010. Segregating Sound: Inventing Folk and Pop

Music in the Age of Jim Crow. Durham: Duke University Press.

James, Robin. 2017a. “Is the Post- in Post-Identity the Post- in

Post-Genre?” Popular Music 36 (1):21–32.

———. 2017b. “Songs of Myself.” Real Life, May.

http://reallifemag.com/songs-of-myself/.

Roberts, Tamara. 2011. “Michael Jackson’s Kingdom: Music, Race,

and the Sound of the Mainstream.” Journal of Popular Music

Studies 23 (1):19–39.

Rose, Tricia. 1990. “Never Trust a Big Butt and a Smile.” Camera

Obscura 8 (2 23):108–31.

Smialek, Eric. 2015. “Genre and Expression in Extreme Metal

Music, Ca. 1990–2015.” PhD Dissertation, Montréal: McGill

University.

Stoever, Jennifer Lynn. 2016. The Sonic Color Line: Race and the

Cultural Politics of Listening. New York: NYU Press.

Page 12: #genre · “#genre.” Essentially, #genre captures the adjectival quality and in-between-ness of the seemingly-flattened stylistic world of popular music categories by exploring

Thomas Johnson #genre SMT: Nov. 2, 2017

11

#genre

ABSTRACT

“Genre is dead!” The sentiment resounds throughout current popular-music

critic-fan and artist discourses, as developments like predictive algorithms,

professional playlist curators, and ubiquitous access all throw wrenches into

traditional machines of musical categorization. Recent sociological work on

the increasing eclecticism of musical tastes appears to support this perspective,

flattening conventional boundaries between kinds of music and classes of

people.

But in this talk, I argue that such omnivorousness of musical proclivities

doesn’t obviate popular music genres; instead, it hints at a deeper shift in genre

ordering. To address this change, I explicitly theorize the work genre does in

the smooth and striated spaces of popular music with a new concept I call

“#genre.” Essentially, #genre captures the adjectival quality and in-between-

ness of the seemingly-flattened stylistic world of popular music categories by

exploring clusters of related artists, genre tags, and playlist constituency.

My methodology approaches this topological change by excavating the kinds

of linear genre-fabric that Spotify weaves through its platform, investigating

relational algorithms and proprietary metadata. To do so, I use original Python

scripts to access and parse Spotify’s metadata, quantitatively assessing the

various kinds of connections that the streaming service generates. I compare

these results to demographic biases to problematize notions of a “post-genre”

musical landscape, nudging genre discourses away from conventional

phylogenetic cartographies or nested hierarchies and towards lateral and

multiple models. My methodology and conception of #genre show how

classification continues to guide all parts of the 21st-century popular music

machine, demanding a renewed investigation.

tom-johnson.net/stuff/smt2017