Music Apps are always finding new ways to understand the kind of music one listens to or prefers. The music algorithm not only monitors the music history but also looks at the reason and time frame of someone listening to a specific genre.
Some of these music apps are made of playlists made by other people that you pay for, but a vast majority rely on their algorithm in addition to the pre-created playlists. Apps that use these algorithms include Spotify, Apple Music, Audiomack, and Boomplay.
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What is a Music Algorithm?
An algorithm is a set of mathematical rules specifying how a specific group of data behaves and performs. When it comes to music apps, these mathematical sets of rules help determine what music is preferable by who, in which location, and on which days.
How Do Music Algorithms Work?
Taking the example of Spotify, by October 2019 it had over 248 million active users. This big number shows how effective the algorithm is when it comes to attracting traffic.
The first step to kicking it off is the Recommendations screen. This is governed by an AI system called Bandits for Recommendations or Bart. This system starts recommending songs based on previous listening activities. In addition to this, it includes new music in the lists that it assumes listeners will like to keep them from listening to the same music again and again.
After this, two concepts come into play, exploit and explore. These are the key to many of these apps’ recommendations. When exploiting, the app makes use of each and every activity produced by a user. This activity may include the user’s listening history, songs skipped, created playlists, social media platforms, and location. With this information, the app is able to recommend music.
When exploring, the app studies the surroundings. When doing this it searches for playlists and artists similar to those you always listen to considering their popularity in the area and other related works. Using this data and more recommendations best suited for you are presented.
Explained below are some of the ways the algorithm recommends music;
The 30-sec rule: These apps also look at the duration of time one spends listening to a song. If it is more than 30 seconds, the platform checks it on the list of recommendations. Therefore, the longer one spends listening to a song the better their suggestions will get. To get the best from these apps, it is important not to spend more than 28 seconds on a song you don’t like.
Recommending New Artists by Analyzing Audio: Recommending music liked by other people might not work for new artists and might bury their work. What the apps do in this case is to analyze the audio and let the audio analysis algorithm recognize different desirable characters in the music. This in turn makes it easy for the app to recommend new artists based on the familiarity of the sound.
Recommendations in Automatic Playlist Continuation: These apps play a song automatically after the current one ends. This is possible because of a feature in the algorithm that analyses the songs in the playlist of choice and attempts to predict the music to be played next.
Recommendations Using Locations: Data provided by a user including age location and gender is also used to determine whether a user’s music taste changes when they change location. When using this, the apps study the kind of music people listen to in particular areas and then compare this data against the group of people who have recently moved to that area. This shows that location does affect a user’s music taste.