AI Music: 4 Ways Artificial Intelligence is Used in the Music Industry Now
What comes to mind when you think of ‘AI music’? The most common association is AI-generated music. The majority of people imagine abstract compositions created by a robot, machine, AI-based application or service. While this is a valid response and fair association, composing and generating are not the only music-related tasks AI is used for.
Since ‘AI’ became a buzzword in the past few years, it may seem like artificial intelligence was invented only recently but it couldn’t be further from the truth. In fact, AI systems and devices have existed for about 60 years, since the advent of the computer, and henceforth have become commonplace in a variety of fields.
Artificial intelligence doesn’t ride the hype wave for no reason, it’s popularity now is a direct result of many years of study and testing based on existing scientific theories.
The application of AI in one area of study affects many other areas, including music. Because of that, AI devices continue to evolve at a rate unprecedented in human history.
Today more than ever, the technology is rich for use in teaching, creating, or evaluating music. Let’s take a look at four major ways AI is now used in the music industry.
#1 Music Recommendation
Have you ever thought about how streaming services manage to accurately recommend music that you might like? How does Spotify know you so well? Such a personalized listener experience is created with music recommender systems powered by artificial intelligence.
Major music service providers, such as Spotify, Apple, Pandora, and Amazon, use AI to analyze the preferences of their listeners and ‘predict’ new favorite songs. AI-driven recommendation engines are used by music streaming platforms to enhance their services. By utilizing the technology, they can understand the tastes of their customers and help them discover music with quality recommendations.
For example, in order to create accurate recommendations for you, Spotify’s AI analyzes the listening history of the entire subscriber base against the songs in your heavy rotation. AI compares the playlists you frequent to the playlists of other Spotify subscribers to locate users with tastes that are close to yours. After that, the AI finds song recommendations based on your own taste and on the music that users with similar tastes listen to.
Both the ‘Discover Weekly’ and ‘Release Radar’ playlists on Spotify are actually algorithm-based features. These automatically generated weekly playlists are highly praised for their spot-on recommendations. There is no magic behind them, only powerful AI technology.
#2 Music Generation
The generation and composition of music are, without a doubt, the most popular and widely known applications of AI in music right now. In this field, artificial intelligence becomes an invaluable partner to any human being that wants to create music in collaboration or get inspired.
While AI-generated music seems like a lazy and ‘weird new’ way of creation to some, David Bowie, one of the most unique and original musicians, used the AI-like Verbasizer script to generate the lyrics for his songs back in the 90s. Check out the clip of Bowie discussing his creative process utilizing Verbasizer here.
There are a lot of outstanding moments in the history of music co-creation by artificial intelligence and human artists. In 2017 musician Taryn Southern composed an entire album in collaboration with AI; at the beginning of 2020, Björk worked with Microsoft to create AI-generated music that changes with the weather. Sometimes AI even goes solo, like AIVA, a deep learning music composing service, that released two albums and became the first virtual composer recognized by music society.
However, AI music generation isn’t as trailblazing as it seems. Essentially, it just provides access to sophisticated yet easy-to-use tools and limitless tunes to play around with. Whether a musician or not, you can spark your creativity and have fun with AI-based services for music generation like Amper, AIVA, and Boomy.
#3 Music Editing
Before songs become available for our listening pleasure, they go through numerous editing steps. Many hours of attentive listening, making mixing and mastering choices, with each changing the final sound of a song or a record. It’s a lengthy process that requires a lot of resources from human specialists — another music-related task that can be accelerated and enhanced by artificial intelligence.
LALAL.AI, an AI-powered stem separation service, aids in deconstructing mixed songs into their constituent contributions, vocal and instrumental tracks. Machine learning algorithms help to isolate each stem accurately and speed up the entire track splitting process to mere seconds. Separated vocal and instrumental stems can be used to create song covers, DJ mixes, karaoke backtracks, etc.
Smart: EQ2 is an AI-driven service users can utilize for music equalization. It automates the process of balancing out the specific frequencies of each track on an album or an EP to make the tracks complement each other. The EQ2 machine learning algorithm determines details that may pass the human ear, automatically corrects tonal imbalances, and increases the clarity of mixes.
Izotope and Landr use machine learning to replicate the processes performed by mastering engineers. Both services utilize the technology to automate the final mix preparation process and provide almost instantaneous mastering results beyond the capabilities of any human mastering engineer. Though the majority of the process is automated, users still have control over their mixing preferences and sound influences.
#4 Music Analysis
Music analysis is a process of retrieving music data and breaking songs down into their characteristics. It helps musicians, labels, producers, publishers, and playlists curators organize and recommend music. With the help of AI, the analysis can be performed significantly faster and more accurately.
The artificial music intelligence of Cyanite listens to millions of songs in just minutes and simultaneously derives information from music to give each track specific characteristics. The service uses two types of analysis: symbolic analysis gathers information about the rhythm, chord progression, harmony, etc. while audio analysis deals with timbre, genre, and emotion of music.
AI-based music analysis services can detect the most emotionally impactful track out of a playlist, album, or other song compilations. It helps musicians select the best track for a single release as well as aids playlist curators create the most soul-crushing playlists. The services also function as recommender systems for music streaming services due to their ability to detect similarities in BPM, mood, and style between tons of songs.