Technology has always been influencing the way we live and do business, but its impact has become even greater in the 21st century. Today, new systems such as machine learning and Artificial Intelligence (AI) are reshaping entire industries and music is by no means an exception here.
According to the report, the global recorded music market grew by almost 10% in 2018, making it the fourth consecutive year of growth. Such a result would be impossible without AI-powered streaming services that essentially saved the descending music business.
Now, many people are curious about one question: How much does AI influence changes in the music industry? It’s a complex topic, so keep reading to learn the effects AI leaves on labels, musicians, and listeners.
Ways AI Impacts Music
AI has a more or less unlimited potential to grow and evolve, so it’s impossible to guess its impact on the music industry in the decades to come. What we are able is to point out the existing trends and explain how AI already influences music as we know it today. Let’s see six examples here:
The rise of streaming platforms
Have you ever heard of Spotify or Pandora? Okay, you know the question is rhetorical because everybody is aware of music streaming services these days. The two major streaming platforms attract millions of users who create over five million playlists a day on Spotify alone.
Of course, streaming platforms could not function properly without AI-powered technologies. Such systems rely on machine learning and language processing to identify and categorize songs, albums, and artists.
The likes of Amazon Music and Pandora are able to process gigantic data volumes in real-time, which is the basic precondition of their functioning. However large, a team of human analysts could never handle so much information, but AI networks do it effortlessly and thus allow music streaming services to flourish.
Music-sharing networks become search engines
This trend goes hand in hand with the previous one. As streaming services and video- and music-sharing platforms keep growing, they are taking the form of full-time search engines.
Although it’s not primarily designed for music streaming, YouTube gives us a great example here. Namely, this platform is already the second-largest search engine in the world. The network is keyword-driven because it’s the only way to process billions of videos and display the right results in a timely manner.
Needless to say, the search engine feature is fully based on AI and resembles Google, Yahoo, and similar platforms. The only difference is that people use Google to find all sorts of things – from content writing agencies like AssignmentHelper.com.au or UK essay all the way to news and tutorials – while music streaming networks concentrate on songs and composers exclusively.
AI enables personalization
If there is only one thing that makes AI special, it would certainly be its ability to personalize users’ experience on music streaming platforms. The new form of technology can analyze every aspect of listeners’ behavior – content history, favorite artists, genre preferences, and many more.
But the analysis goes way deeper than that because AI also evaluates the smallest details such as melodies, vocals, chords, etc. Such a complex analysis gives music streaming services the opportunity to design fully personalized recommendations and open new perspectives for their users.
Almost every platform has its own recommendation format. For example, Spotify created a popular Discover Weekly program which makes personalized playlists every Monday for each user individually. This level of personalization was way beyond imaginable only a decade ago.
Discover new artists
We already mentioned how AI helps listeners to personalize the experience and enjoy streaming services in a way that seems suitable for their personal preferences. However, this process goes two ways because it also benefits music makers as well.
Namely, music platforms upload fresh content every day and it’s getting increasingly difficult for new artists to get discovered. AI solves the problem – at least to a certain extent – using personalized recommendations.
Mark Jacobson, a music analyst and a creator of VelvetJobs and ResumeGo reviews, explains how the process functions:
“While it’s almost impossible to find relevant content among so many songs, users can now discover interesting artists based on tailored playlists. This gives young and talented musicians a big boost because they could hardly deserve attention any other way.”
AI is making music
If this statement sounds incredible, that’s because it is! AI is not only affecting the music industry indirectly but it is also contributing to it first-hand. This technology is already so powerful that it can analyze the structure of songs, vocals, chords, and forms. As a result, we get AI-composed music.
If you don’t believe us, listen to this tune. It’s a two-minute soundtrack composed entirely by AIVA Technologies, a digital intelligence system geared toward cinematic music making. This is only one case of AI composing, but you can find hundreds of similar examples online.
Musicians need tech skills
Now that AI is able to analyze and produce music on its own, it is clear that musicians need to start using new technologies for composing purposes. It’s a rising trend that is yet about to grow and expand globally, but it has the potential to drastically change music production as we know it. Artists who realize this fact first and become tech-savvy will probably achieve big success and outperform colleagues working in the same genre.
AI represents a revolutionary technology that strongly impacts almost every field of life and work in the 21st century. The same goes for the music industry, which is why we wrote an article about its impact on modern composers, fans, and labels.
What do you think about the new music trends powered by AI?
Do you think technology can further improve the way we create and consume music?
Let us know in comments and we’ll be there to discuss this exciting topic with you!
Tags AI, Artificial Intelligence, Computer generated music, Machine Learning, digital intelligence