I’m working with the Spotify API and running into an issue when fetching an artist’s discography. The API returns a mixed list that includes both full albums and individual singles, but I need to separate these two types of releases.
When I make a request to get all releases from an artist, I get back everything together. However, when I check the same artist in the actual Spotify app, I can clearly see which items are proper albums and which ones are just single tracks.
Is there a parameter or field in the API response that helps identify whether a release is a complete album or just a single? I’ve looked through the documentation but can’t find a clear way to make this distinction programmatically.
Any suggestions on how to properly filter these results would be really helpful.
combine album_type with track duration - singles rarely hit more than 4-5 tracks and stay under 20 minutes total. singles also get updated way more often than albums in spotify’s database, so compare the release_date with recent changes if you’re working with popular artists.
I’ve been working with artist discographies for two years now, and here’s what I’ve learned: you can’t rely on just one data point. The album_type field is a good start, but it gets things wrong all the time. My method involves checking the explicit field in conjunction with the track count. Promo singles usually release as explicit versions first, followed by clean versions later. The label field proves invaluable as well; major labels often implement different distribution deals for singles versus full albums. Additionally, I examine the copyrights array, since singles typically involve simpler copyright structures compared to albums, which often entail multiple publishers. This combined approach has significantly improved my playlist tool’s accuracy, particularly for indie artists where Spotify’s categorization can be quite unreliable.
I ran into this exact problem building a music analytics dashboard last year. Skip trying to use single fields - you’ll get garbage results. I built a weighted scoring system that looks at multiple things: tracks under 15 minutes are usually singles, albums typically run over 20 minutes. Check the genres array too - compilation singles often have mixed genre tags while albums stay consistent. Singles also tend to have way higher popularity scores than deep album cuts. Don’t forget track names either - singles love their feature credits and remix labels. This approach dropped my error rate to under 5% when I tested it against hand-verified data.