MUSIC DESCRIPTORS
Gracenote Global Descriptors Power Meaningful Discovery
Every streaming music service on the planet is driven by data. It’s an essential ingredient to today’s music experiences. And Gracenote has descriptive data for practically every song ever recorded. All of this descriptive information is used to make deep connections between artists and tracks, creating radio stations and playlists that share common musical characteristics.
Gracenote has the industry’s most comprehensive source of descriptive music data available today.
Genres
Style Descriptors
Languages
Global Descriptor System
Gracenote has the world’s deepest source of descriptive information for music having classified millions of tracks at the artist level by Genre, Origin, Era, Artist Type and Language, as well as at the recording level by Mood, Tempo and Style. This data fuels the engines for the world’s most popular music services, helping fans uncover more of the music they love.
Scalable Solution
Gracenote combines editorial experts, real people around the globe with a passion for music, with advanced machine learning technology to extract and classify musical characteristics. The combination of human and machine enables Gracenote to scale to tens of millions of tracks.
Global Genre System
The Gracenote Genre System features more than 2,451 unique genres, with 438 track-level sonic style descriptors, and supports more than 480 languages. This proprietary music classification system is built with a global audience in mind, presenting genre and style hierarchies that are regionally relevant, such as J-Pop in Asia and Bollywood in India.
Product Benefits
- Gracenote has the industry’s most comprehensive source of descriptive music data available today.
- Descriptive data is delivered to Gracenote music customers to match their catalog and unique IDs.
- Track-level descriptors for Mood, Tempo and Style are derived from audio analysis, with multiple values assigned to track and weighted to reflect the music.
- Flexible delivery via standard data files.
Music Datasets
Genre of the song (Rock, Hip Hop)
Era the song was recorded (1980s, 2000s)
Origin or region most associated with the artist (London, New York)
Language of the artist (English, Spanish, Portuguese)
Artist type (Mixed, Female, Male)
Mood (Rowdy, Somber)
Tempo (Fast, #BPMs)
Style (Industrial, Jump Blues)
Gracenote Descriptor System
2,451 Genres, 438 Style Descriptors, 480 Languages
