the ListenBrainz data set

I’ve been using ListenBrainz as my primary way of keeping track of my listens for a couple years now—I don’t recall exactly when I created my account, as I’ve imported my listens from Spotify, but my music players have been pushing data to ListenBrainz for a while now either way.
- I made an account because I was dissatisfied with being tied to a single service for all my music. especially since I’ve been listening to more and more music outside of streaming services, which means my listening data wouldn’t show up in Spotify.
- I chose ListenBrainz over a proprietary service, because I like the idea of building a huge data set of what people are listening to. and adding my own two drops of water to that data set feels really cool. not only can my data be used for greater scientific purposes, but I can also use my own data for my own scientific purposes.
- obviously there’s the question of data ownership; and technically speaking, I don’t own my data on ListenBrainz. but honestly I don’t mind that so much, since it’s an open source service, that lets me export my data whenever I want to.
- you can find my account here.
below are some things I’ve done with my data, as well as ideas about what I could do with it in the future. when I have the time, patience, and motivation, of course.
:TODO: the importing of my listens from Spotify
- I’ve never published the code, but I wrote a little script that took my exported Spotify data (thanks, GDPR!) and imported it into ListenBrainz. not without faults though. it’d be nice to write up more about it.
:TODO: nerdsniped by Spotify Wrapped
- it’s this; it’d be nice to write up some more about it.
:TODO: a story told by a thousand tracks
- I’d like to use my data set to take my top 1000 most listened tracks, and sort them in chronological order—tracks I listened to earliest come first—to generate a sort of life’s soundtrack.
  - I already have such a curated playlist like this on Spotify, but I’m curious how such a playlist would look, were it generated from objective listen data.
    sans some inaccuracies of course, since Bogdan Raczynski’s boku mo wakaran got imported all as one track. this is because the individual tracks are untitled, and ListenBrainz failed to differentiate them, despite my Spotify data saying “Untitled 1,” “Untitled 2,” and so on, with track indices. so I’d have to skip it, which is a real shame, since it’s a pretty cool album.
:TODO: diminishing listens, or longer attention span?
- in 2018, I listened to 25652 tracks. in 2019, I listened to 34904 tracks. in 2020, I listened to 29248 tracks. in 2021 it was 24451, in 2022 it was 22437, and in 2023 it was 22002.
- we’re already past half of 2024, and I’d only listened to 10536 tracks so far as of typing this. if I keep up the same pace throughout the other half of the year, and if I’m doing my math correctly, that’d mean at the end of the year I will have listened to around 15000 tracks. that’s quite a bit less than 2023, isn’t it?
- I wonder why the numbers are getting lower and lower each year. is it because I listen to less music overall, or is it because I listen to longer music? I strongly presume it’s the former, as a lot of things have changed for me in 2024—I moved out to live with my girlfriend, and that means spending more time on housework and other mundane things.
  - until recently I didn’t have a pair of good wireless headphones to listen to music while cooking, cleaning, or doing the laundry, so that reduced my listen count quite a bit.
  - funny how in July there was a large spike of 1747 listens, where other months seem to be oscillating around 1000 listens on average. I don’t know what that was about.
  - I do wonder where the 2000 listens per month from last year went though.
- either way, I’d like to derive a data set that’s counted by minutes listened to iron over this, similar to what I did with Spotify Wrapped in 2022.