The truth is that AI is now being used at every stage of the music creation process, from composition tools during the creation phase, to mastering software during the production phase and even highly personalized music recommendations during the distribution phase.
So let’s dive into it to see how exactly it does so.
1. What is artificial intelligence?
It’s an oxymoron, because how can a lifeless thing like a machine be intelligent? But behind these machines are human beings who ‘feed’ them the tools they need to think. AI is therefore a branch of computer science that aims to create systems capable of performing tasks that require thought. This is what differentiates it from simple robotization: the tasks require human intelligence, which means that they can now range from learning to decision-making, including understanding language, solving problems, and recognizing patterns.
And in the music industry, AI is being utilized to create music and understand listener preferences, as well as to predict future trends based on current popularity charts and historical data. It’s also being used in music education and many a musical arts student are navigating the music industry with AI music now.
This is an enormous technological revolution that, while it has led to advances in many areas, has also given rise to a number of questions and fears.
2. The growing role of artificial intelligence in AI-generated music creation.
We are listening to more and more AI-generated music mainly because AI music is rising in the music industry. And that’s because the potential of artificial intelligence is infinite, as is the amount of music it can create. In fact, algorithms can compose pieces of music that cover every musical genre, and we’re not just talking about simple melodies, AI can generate lyrics, structures, complex harmonies, rhythms, and beats. You can simply provide it with the emotion you’d like to trigger with the music and AI can generate music that will provoke said emotion. It can do so much more than a decade ago because it can now compose a song from scratch and even mix different musical genres, creating a previously unknown musical genre.
That said, like any self-learning system, it requires input, hence the need for human involvement. And it’s this fusion of human creativity and AI capabilities that promises to produce innovative music in the future. However, this merger will also lead to challenges concerning traditional notions of copyright and ownership, but that is not the subject of this article!
As for AI-driven software, there are a few, such as Google’s Magenta or OpenAI’s MuseNet, that can mix different musical genres to compose complex pieces that might not even have been imaginable to a human composer. To do this, they analyze a huge amount of existing material provided to them to analyze styles, patterns, and structures, and then be able to reproduce something similar.
In addition, AI is able to create personalized music for each listener, depending on their mood or activity. This is what platforms like Aiva and Endel are doing.
And AI also impacts music marketing strategies by analyzing data and predicting consumer behavior, transforming how music is marketed and consumed.
Ok so now that we’ve seen that our listening habits are being influenced by the sheer amount of AI music being created, let’s look at how, in the distribution phase, AI algorithms are influencing the way we listen to music.
3. The way distribution platforms use artificial intelligence to analyze music listening habits.
The emergence and growth of streaming services made all music available to everyone, everywhere.
And today, AI has brought with it the era of hyper-personalization through tools such as customized music recommendations with AI playlists and predictions based on user activity and song characteristics. On top of that, these AI-generated playlists help in discovering music, including cultural traditions and lesser-known genres (which is great for a more diverse and inclusive music industry – but more on that a bit later).
Streaming platforms such as Spotify, Apple Music, and YouTube Music all use AI algorithms, and these algorithms process vast amounts of data about things like song length, genre, tempo, and even the time of day when users are most active. Knowing all this data, the algorithm can then recommend songs and playlists that match individual tastes as well as particular moods and activities. Whether it’s a workout, a study session, or a relaxing evening, the AI can create playlists tailored to that particular experience. For example, it can introduce you to your next workout tune.
Let’s first look at playlists. It used to be that to make playlists, people made something called mixtapes by recording individual songs from tapes or the radio onto another tape. Now, streaming platforms are leveraging AI algorithms to suggest songs to their users based on their previous listening habits.
Spotify’s ‘Discover Weekly’ and ‘Release Radar’ playlists are good examples of AI-generated playlists or AI-influenced playlists.
The truth is that we are now used to listening to music that suits our tastes and even our mood and we don’t want to listen to the same songs over and over again and we don’t want the same songs regularly playing. But how exactly can AI-powered music recommendation systems deliver this?
Now let’s mention how AI can predict trends (as well as create them). Because as well as knowing what a user wants to hear, AI can also predict the next big hit that everyone will be listening to, and introduce listeners to new music that they might not otherwise have discovered. Indeed, musical preferences are changing, and we are seeing greater diversity in playlists and more individual tastes in music. This is because AI can expose people to a wider range of music and cultural traditions and the usual gatekeepers, such as radio stations, record companies, and the like, are no longer there, which means that young people listen to a much larger catalog of music than previous generations.
This counters one of the main concerns about AI in music creation, namely cultural homogenization with more and more music sounding eerily similar. But this is counteracted by exposing users to more diverse music that they would be less likely to discover for themselves, whether it’s Japanese metal, Latin ballads, glitch hip-hop, Nigerian popular music, or traditional Korean music. All of this is backed by research as a recent study highlighted the presence of diverse and eclectic musical preferences among adolescents and students whose musical education backgrounds are changing and expanding.
In conclusion, AI music has changed and continues to change the way we create, discover and experience music. It’s safe to say that this is just the beginning and it’s a revolution worth paying attention to. Afterall, it is known for pushing the boundaries of all fields, and the music industry is no exception. But we must remember that the aim is not to replace man-made music but to inspire and support musicians in their creativity.
And when it comes to listening to music, a question you may want to start asking yourself is whether you like a song because you really like it, or whether you like it only because the AI has fed you enough similar songs for familiarity to breed appreciation.
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It’s time to send your track on Groover 👇
