What Makes Music Matter Today? Following The Data!
March 30, 2018
The end of genre? Well...
Today, companies are trying to make decisions relying on as few assumptions as possible. Whereas in the past, the industry relied primarily on sales and how often a songs were played on the radio, they can now see what specific songs people are listening to, where they are hearing it and how they are consuming it.
On a daily basis, people generate 2.5 exabytes of data, which is the equivalent to 250,000 times all of the books in the Library of Congress. Obviously, not all of this data is useful to the music industry. But analytical software can utilize some of it to help the music industry understand the market.
The Musical Genome, the algorithm behind Pandora, sifts through 450 pieces of information about the sound of a recording. For example, a song might feature the drums as being one of the loudest components of the sound, compared to other features of the recording. That measurement is a piece of data that can be incorporated into the larger model. Pandora uses these data to help listeners find music that is similar in sound to what they have enjoyed in the past.
This approach upends the 20th-century assumptions of genre. For example, a genre such as classic rock can become monolithic and exclusionary. Subjective decisions about what is and isn’t “rock” have historically been sexist and racist.
With Pandora, the sound of a recording becomes much more influential. Genre is only one of 450 pieces of information that’s being used to classify a song, so if it sounds like 75 percent of rock songs, then it likely counts as rock.
Meanwhile, Shazam began as an idea that turned sound into data. The smartphone app takes an acoustic fingerprint of song’s sound to reveal the artist, song title and album title of the recording. When a user holds his phone toward a speaker playing a recording, he quickly learns what he is hearing.
The listening habits of Shazam’s 120 million active users can be viewed in real time, by geographic location. The music industry now can learn how many people, when they heard a particular song, wanted to know the name of the singer and artist. It gives real-time data that can shape decisions about how – and to whom – songs are marketed, using the preferences of the listeners. Derek Thompson, a journalist who has examined data’s affects on the music industry, has suggested that Shazam has shifted the power of deciding hits from the industry to the wisdom of a crowd.
The idea of converting a recording’s sound into data has also led to a different way of interpreting this information.
If we know the “sound” of past hits – the interaction between melody, rhythm, harmony, timbre and lyrics – is it possible to predict what the next big hit will be? Companies like Music Intelligence Solutions, Inc., with its software Uplaya, will compare a new recording to older recordings to predict success. The University of Antwerp in Belgium conducted a study on dance songs to create a model that had a 70 percent likelihood of predicting a hit.
Of course, YouTube might tend to cluster songs by genre in its search algorithm, but it’s increasingly clear that the paradigms that have defined genres are less applicable now than ever before.
What happens next?
Even as new information becomes available, old models still help us organize that information. Billboard Magazine now has a Social 50 chart which tracks the artists most actively mentioned on the world’s leading social media sites.
In a way, social media can be thought of as analogous to the small musical scenes of the 20th century, like New York City’s CBGB or Seattle’s Sub Pop scene. In Facebook groups or on Twitter lists, some dedicated and like-minded fans are talking about the music they enjoy – and record companies want to listen. They’re able to follow how the “next big thing” is being voraciously discussed within a growing and devoted circle of fans.
Streaming music services are increasingly focused upon how social media is intertwined with the listening experience. The Social 50 chart is derived from information gathered by the company Next Big Sound, which is now owned by Pandora. In 2015, Spotify acquired the music analytics firm The Echo Nest, while Apple Music acquired Semetric .
Songwriters and distributors now know – more than ever – how people listen to music and which sounds they seem to prefer.
But did people like OMI’s 2015 hit “Cheerleader” because of its sound and its buzz on social media – as Next Big Sound predicted? Or did it spread on these networks only because it possessed many of the traits of a successful record?
Does taste even matter? You’d like to think you listen to what you enjoy, not what the industry predicts you’ll like based on data. But is your taste your own? Or will the feedback loop – where what you’ve enjoyed in the past shapes what you hear today – change what you’ll like in the future?