The amount of data we generate online every day is staggering. There’s an estimate that we create more information every 60 seconds than the whole human race was able to generate from 10,000 BC to 2000 CE. That’s a LOT of information. Big data, indeed.
Let’s apply this to the music industry.
In the Olden Dayes, the data currency of the realm included sales and radio airplay. Those numbers were combined to create a multitude of charts and charts became the way the industry kept score. They measured how much exposure a given song or album was getting. That was perfectly fine until we got deep into the digital era and especially the era of streaming. The Next Web takes it from here:
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 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.
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 a70 percent likelihood of predicting a hit.
So what’s happening with this information? The industry is now using this data to predict The Next Big Thing. Keep reading.