Spotify has introduced a new feature called SongDNA that helps users explore how songs connect with each other. The feature works inside the mobile app and shows relationships between artists, tracks, and creative influences. Spotify aims to give listeners a deeper understanding of the music they play.
Spotify announced SongDNA as a beta feature available within its mobile app. Users will see it as a card below the currently playing song. From there, they can explore details about the track and discover how it links to other music.
Don’t miss the best of The Mac Observer
Set us as a preferred source and our Apple reporting ranks higher in your Google Search results and Discover feed — one tap, no account changes.
Spotify explains, “By simply tapping the SongDNA card, you can explore the writers, producers, and collaborators behind a song, see samples and interpolations that shaped its sound, and browse the covers it inspired.”
The feature allows users to move from one connection to another. For example, listeners can tap on a producer or artist and then explore other songs linked to them. This creates a chain of discovery that helps users find new music while understanding how different works connect.
Spotify adds, “It’s an interactive way to follow the connections between tracks and see how artists, eras, and genres intersect, giving you a deeper understanding of how what you’re listening to came together.”
How to access SongDNA
Spotify says users can access SongDNA directly from the Now Playing screen. After opening a song, they need to scroll down to find the SongDNA card on supported tracks. From there, they can tap and start exploring collaborators, samples, covers, and more.
Spotify confirmed that SongDNA is rolling out to Premium subscribers on both iOS and Android starting today. The company plans a gradual rollout through April, so some users may not see the feature immediately.
Spotify recently introduced other updates, including tools to improve playlist transitions and experiments that let users adjust recommendation algorithms.
Discussion