Before Spotify can recommend music for you, its algorithms must have a quantitative way to describe each of the millions of tracks in its database.

Creating an image for each music track is an interesting problem in itself. Spotify has invested in enough research to find the best models to describe every album in its catalog.

, Spotify uses two main methods to create representation: content-based filtering and collaborative filtering.

 of these methods do and how they work together to create a complete musical representation.

Let's look at what each

Content-based filtering aims to describe each track by examining telemarketing lists the track’s actual data and metadata.

When artists upload music to Spotify’s database, they must provide the music file itself, as well as additional information or metadata. Metadata includes the name of the song, the year it was released, the track record, and even the length of the song itself.

When Spotify receives these files, it can quickly use the provided metadata to categorize songs. A British rock album from 1989, for example, can be placed in various playlists such as “Classic British Hits” or even “Rock Songs from the 80s”.

However, Spotify goes a step further and analyzes the raw audio file itself to get some quantitative metrics from the track. If we look at the we can see a few of these metrics.

To deal with this problem

For example, the API includes an energy metric that measures the “visual measure of intensity and activity.” According to the documentation, the metric is derived from several characteristics including dynamic range, perceived loudness, and timbre. Using this metric, Spotify can group high-energy songs together and use them as recommendations for users who listen to high-intensity music.

In addition to energy, Spotify also determines the animation of the track, a metric that detects the presence of listeners in the recording. Valence is a measure that defines how positive a track is. A high valence sound indicates cheerful a Bulk Lead nd happy music, while a lower valence sound indicates sad, depressed or angry music.

Spotify also has another interesting analysis algorithm that describes the temporal structure of the track. One track is divided into different sections: from sections (chorus, bridge, instrumental solo), to the individual beats themselves. You can check how Spotify describes the structure of your favorite songs by using thi sends a request to the Spotify API.