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In 2014, Snoop Dogg asked 50 Cent what he missed most about the past, when hip hop “was what it was.” 50 Cent replied “authenticity,” which led to Snoop’s infamous imitation of today’s rappers:

All these rappers sound the same

In short, originality has given way to homogeneity, with artists copying whatever’s in fashion (especially as rap music becomes today’s pop music).

Regardless of whether Snoop is right, the same could be said of 1990s hip hop. Or 1980s pop (with pervasive synths/808 beats). Or 1960s rock, when the Beatles (and their sound) permeated radio. The music industry will ride a trend (if it sells), and hit songs converge on a style.

Is there something unique about today’s hits—artists taking fewer risks and creating a narrower range of sounds?

Every Year...

...The Hit Songs

...Sound Alike

measuring the similarity of music

First, we need a way to measure whether popular music in our parents’ generation (and every generation prior) was more musically diverse than today. What defines a song’s sound—its musical composition?

One such dataset is the Music Genome Project, which is the engine powering Pandora. The data is produced by music analysts, who score a song on 400 attributes that span genre, vocals, tempo, key, and instruments.

For example, here are #1 songs from the 1980s by their prominence of synthesizers.

Billboard #1 Hits by Synth Usage
Pandora’s Instrument Prominence of #1: Synths

1984 was peak synth—every #1 song prominently featured synthesizers, a strong case for music homogeneity in the mid-’80s. But on closer examination, “Jump” by Van Halen and “I Just Called to Say I Love You” by Stevie Wonder are flush with synths, but different musically.

A complete picture would need to consider hundreds of musical genes. And not every gene is created equally: Should guitar data matter more than flutes? Should tempo have more influence than key? In short, what data points are best in describing a song?

Reducing a song to 8 data points

In 2005, Tristan Jehan, a PhD student at MIT, published his dissertation, “Creating Music By Listening,” a framework for AI-created music. As Jehan put it simply, “You feed it new music in and it generates new music out.”
The more immediate implication of Jehan’s work has not been the human-to-robot gift of art, but reducing a song to a small set of data points that say something about the song generally, such as “valence” (roughly the happy to sad spectrum) and “energy” (arousing to soporific). Shortly after completing his dissertation, Jehan co-founded a company called EchoNest, and the data became a pillar of Spotify’s recommendation systems, determining music similarity and accurately suggesting songs that sound alike.

This data is publicly available (the Music Genome Project version is confidential), and to measure whether songs sound similar, we’ll calculate the differences in EchoNest’s 8 data points for top songs in the Billboard Hot 100, a peer-reviewed method employed by other music researchers. In theory, the songs with most similar EchoNest values should sound similar as well.

Calculating the Most Similar/Different #1 Songs
We subtract each song’s EchoNest results and find the song pairs with the smallest and largest differences.
The two most similar songs from 2007 to 2011 were Katy Perry’s “Teenage Dream” and Kesha’s “We R Who We R” , and they indeed sound alike.
To make a claim about an era’s musical homogeneity, we just need to calculate the average distance in EchoNest data points for all popular songs: How far away is a song from every other in that period?

The Case for Homogeneous Music

The musical differences among hits on the Hot 100 is trending downward.
The two most-similar hits, by era, are charted for comparison.

EchoNest data for non-#1 Billboard hits via Noah Askin and Michael Mauskapf, authors of What Makes Popular Culture Popular?: Product Features and Optimal Differentiation in Music
The result is a trend toward similarity, with smaller distances among songs. To date, songs that charted between 2012 and 2016 were the most similar, according to EchoNest data.
John Seabrook is a staff writer at the New Yorker and author of The Song Machine, a history of pop music’s last 25 years of unstoppable hit-making sophistication. When he looked at this trend, what did he see?
“What I see,” he said, “is the enormous, quick but large scale shift from the kind of craft-like song-making process of people putting together lyrics and melody in a semi-organic setting to what I call the track-and-hook method.”

Track and Hook

“Track-and-hook” is Seabrook’s coinage for a music-making method that fundamentally distinguishes today’s music-making from all that came before. What separates track-and-hook from its predecessors is how the music is made. The storied, solitary figure working out musical problems at a piano while filling up an ashtray has been replaced by teams of digital production specialists and subspecialists, each assigned to a snare track, a bass track, and so on, mixed and matched and stuck together like Legos.
“The process doesn’t lend itself very well to art,” Seabrook said. Instead, track-and-hook is far more literally factory-like, a mode of production that emphasizes specialization and volume. As the technology writer Nicholas Carr wrote, “The manufacture of pop songs has been so thoroughly industrialized that it makes the old Motown ‘hit factory’ look like a sewing circle.”

Accordingly, the songwriting credits of hits have seen a steady and staggering growth in the number of songwriters credited in Billboard Hot 100 songs.

In the late 80s, the most-common songwriting team consisted of two people, and only 7 songs had more than 3 writers. Today, well over half of songs are written by 4 or more people. Songs with over 10 songwriters are increasingly more common: “Uptown Funk” and “Havana” had 11 songwriters each. With songwriting by committee, one can imagine each individual’s musical quirks are “averaged out,” producing a less unique sound.

The Top 1% of Music Creators

Digital audio-editing software and the ability to easily transfer the sounds over the internet to other writers “suddenly allowed you to create 50 or 100 or more songs in the same amount of time that it would take to create only a few if you sat in a room with people and someone tried to work on a chord pattern and someone else tried to work on words and then you brought a producer in and then you had it arranged,” Seabrook said. A single songwriter can specialize in literally the snare-drum.
In hip hop, track-and-hook is almost an inevitable part of making beats out of other assembled sounds. In pop, it didn’t take off until the mid-1990s, when a Swedish producer named Denniz PoP discovered Ace of Base’s demo and turned it into “All That She Wants” .
Denniz’s lineage most notably includes Max Martin and Luke Gottwald, known as Dr. Luke. Martin arguably did more than any living songwriter to refine the adrenaline-charged bubblegum sound of the past 10 years. Since 1985, he’s the #1 writer and producer by songs that charted in the top 5, which began with “Quit Playing Games (With My Heart)” and “...Baby One More Time” and most recently includes “Can’t Feel My Face” and nearly every Taylor Swift hit since 2012.
Almost in lock-step with the trajectories of musical similarity and the average number of writers behind hits, a shrinking number of writers have become responsible for producing more and more of what charts.
Share of Hits by the Top 10
The 10 Producers with the Most Hits that peaked on the Billboard Hot 100 #1 through #5.
From 2010-2014, the top ten producers (by number of hits) wrote about 40% of songs that achieved #1 - #5 ranking on the Billboard Hot 100. In the late-80s, the top ten producers were credited with half as many hits, about 19%.
In other words, more songs have been produced by fewer and fewer topline songwriters, who oversee the combinations of all the separately created sounds. Take a less personal production process and execute that process by a shrinking number of people and everything starts to sound more or less the same.
Looking forward, will hit songs continue to become less musically diverse?
The dystopian perspective: music streaming allows us to listen to any unsigned artist, yet a few record labels still control 80% of the market (and growing). Hip hop is now the dominant genre, a track-and-hook archetype. Beats are programmed, copy and pasted or downloaded to mimic top producers. Recreating whatever’s fashionable has never been easier.
On the other hand, fewer barriers to entry means every aspiring artist has a chance to compete on originality, perhaps one day diminishing the outsized role of elite producers. While there are plenty of burgeoning Max Martins (e.g., Metro Boomin, DJ Khaled), it’s also the era of “SoundCloud rappers”—labels sign talent after artists have made it big. A young songwriter can find a “Meek Mill-Ace Hood Type Beat” on YouTube, pay a $200 license, and make “Panda” . Technology could create a great musical-diversification event and hits will one day diverge from one another.
The obvious trend is that the Billboard Hot 100 will continue to musically converge, a path that might just be the natural progression of popular culture. Give it enough time and we’ll all be listening to the same thing.

notes and sources

There have been a few academic projects that have discussed homogeneity of music. In 2017, Noah Askin and Michael Mauskapf researched What Makes a Number One Hit, using EchoNest data. They found that “songs that reach the highest echelons of the charts bear some similarity to other popular songs that are out at the same time, but they must be unique in certain ways. That is, they must be optimally differentiated.” Similar to this article, they produced a music similarity score referred to as “typicality.” Unlike our approach, this metric controlled for genre. That is, songs were compared within their genre of hip hop, rock, country, etc. rather than the entire Billboard chart. We had them produce their typicality score for songs reaching the top 10, and the results are somewhat similar to ours (line going up = songs are more similar), though the trend only begins in the 1990s, with songs becoming more diverse from the late-50s to late-80s.
In 2012, this study published in Nature Measuring the Evolution of Contemporary Western Popular Music also measured changes in music homogeneity. The researchers extracted their own features from 17,000 songs that included loudness, pitch, and timbre. In an interview with Reuters, the team had similar results: “We found evidence of a progressive homogenization of the musical discourse...In particular, we obtained numerical indicators that the diversity of transitions between note combinations - roughly speaking chords plus melodies - has consistently diminished in the last 50 years.”
Yet another paper published in 2015, using a similar dataset extracted from audio features, had the opposite conclusion, “Contrary to current theories of musical evolution, then, we find no evidence for the progressive homogenization of music in the charts and little sign of diversity cycles within the 50 year time frame of our study. Instead, the evolution of chart diversity is dominated by historically unique events: the rise and fall of particular ways of making music.” A less academic distillation of the paper can be found in the researchers interview with PBS.
We were only able to attain accurate songwriting and producer credits after 1985 and for the songs entering the top 5, which is why all visualizations rely on this set of data.