How Algorithms Decide Which Music to Play

    How Algorithms Decide Which Music to Play.

    Algorithm engines are strategic assets in platform competition. Because it is a complex science, it is impossible to know exactly how recommendation works. However, there are some broad conclusions we can draw from the data provided by the DPSs (Digital Service Providers).

    Metadata Analysis

    The metadata delivered by Apprise Music with the audio assets is the standard source of information. The algorithm analyzes the artist’s name, album name, track listing, and so on to determine an initial level of proximity between tracks.

    Analyzing Raw Audio Signals

    Apprise Music provides a lot of metadata when audio assets are delivered to platforms. They can, however, add additional data by analyzing the audio track itself by detecting the language of any vocals, its danceability (tempo, rhythm stability, beat strength), energy (loudness, timbre, onset rate), and valence (happy, cheerful, euphoric).

    Analyzing User Preferences

    When content is personalized, recommendations become more effective. The algorithms can understand tastes because they have access to listening data and user libraries. These include explicit indicators such as geography, session lengths, track playthrough, and repeat listens, as well as implicit indicators such as library saves, playlist additions, shares, and skips.

    Each user profile is enriched with all of the data on these preferences, taking into account temporal patterns as well because tastes change.

    General Meaning Analysis; Natural Language Processing Models

    Some platforms consider themes and the general meaning behind a song’s lyrics in addition to the keyword approach to metadata. Algorithms can recognize locations, brands, and people mentioned in the song. User-generated playlists are also a source of information, particularly playlist titles, which can provide additional information and track proximity.
    If a song appears on many playlists with the word “sad” in the title, for example, it can be classified as a sad song.

    Top Tip

     Begin by increasing user library saves through a pre-save campaign, editorial playlists, and advertisements.

     Pitch your release to editorial teams.

     Increase the number of streams in the explore surfaces by optimizing your metadata.

     Check that your Artist Page is up to date. Get creative with your features and marketing to increase your chances of being featured in more playlists and other places, such as the press.



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