Really interesting idea, using images to generate playlists. I’m curious what interpretation is being done on the image.
I find myself in the spotify ‘discovery’ trap getting slowly funneled into a steady universe based on likes from previous weeks’ playlist, where everything is new but also the same. I’ve often correlated music with mood and color and it can be easier to express what you are seeking with colors vs. expressed genre, which is just a broad classification
The lie of music discovery algorithms
171–180 of 198 posts
Re: The lie of music discovery algorithms
#172Music discovery has never worked for me, for the simple reason it's the lyrics, not the music. I listen to anyone speaking truth, (the truth I believe, of course), and that gives me a wide disjoint range of music, but they are all singing about political social truths. Marvin Gaye, Public Enemy, Rage Against The Machine, Beyonce, The Stranglers, The Jam, Psychic TV... It's the lyrics, and now today, we finally have t…
> with the lyrics, with the intellectual content of the music and not just the dressing. > Marvin Gaye, Public Enemy, Rage Against The Machine, Beyonce Thanks for the laugh, man, I needed it.
If you're reacting to the inclusion of Beyonce, I suggest checking out her recent Country music album, which is a political re-envisioning of Country music and to many a direct attack on the comfort seat of White racism.
Re: The lie of music discovery algorithms
#173Way back when Last.FM has its own radio service, I could throw a few random genres and/or artists at it, and it would recommend me pretty much exactly want I wanted every time. I gladly enabled scrobbling in my music players, and it tended to recommend me good stuff every time. Ever since its radio feature got killed, its database has been getting worse and worse. Just now, I tried to search for some things it used t…
> Pandora had decent algorithms for recommending things, but it had such a small library that it would frequently repeat the same handful of albums for anything I searched for. This irks me, as I hate wearing out good music.
I've been a daily user of Pandora for something like 10 years. It's been getting steadily worse the whole time, and especially in the last two years.
I like to create my own station that is "seeded" by a few artists, and then allow the algorithm to do what it wants to play related music. This used to be great, until one day I noticed that it had become stuck playing the same 50 or so songs. This was after about three years of listening to that station on a weekly basis. As an experiment, I created a new station and seeded it again with similar artist. Again, it was fine for 2-3 years until it got stuck on a handful of songs. I did this again recently and it has become stuck within four months. There are even a few of my seed songs that it simply ignores and never plays.
Re: The lie of music discovery algorithms
#174Earlier quoted context omitted.
Spotify has song credits too, just FYI. In my experience, and the reason I left Tidal several years ago, is that they lean heavily into modern hip-hop, compromising relevancy for the sake of promoting "friends of the company" (Jay-Z, Beyonce, etc).
Spotify only lets you read a very reduced version of the credits of a song, and you can't click on the artist to see all of their work. On Tidal, the credits are much more extensive, and you can go deep down the rabbit hole of a producers work in one click. More importantly though, Spotify doesn't directly link artists to bands they're in. For example, on Spotify, Billie Joe Armstrong is credited with his work on Gre…
> For example, you can see just the songs that Paul McCartney wrote (turns out he wrote a track for Drake called Champagne Poetry in 2021... who knew).
Unexpected writing credits like this usually indicate sampling. In this case [0], Drake sampled a song [1] that sampled another song [2] that was a cover of a Beatles song [3].
0: https://en.wikipedia.org/wiki/Champagne_Poetry#Samples_and_c...
1: "Navajo" by Masego
2: "Michelle" by The Singers Unlimited’
3: "Michelle" by The Beatles
Re: The lie of music discovery algorithms
#175Way back when Last.FM has its own radio service, I could throw a few random genres and/or artists at it, and it would recommend me pretty much exactly want I wanted every time. I gladly enabled scrobbling in my music players, and it tended to recommend me good stuff every time. Ever since its radio feature got killed, its database has been getting worse and worse. Just now, I tried to search for some things it used t…
Amazon music does an 'ok' job at recommendations for me, slightly better than spotify, but that's not a high bar. Jango does a better job, or did last time I used it, but it has so little available music that it somewhat self limits itself.
Re: The lie of music discovery algorithms
#176Pandora, Pandora, Pandora. No other service for me worked to reveal new songs and artists I did not know about that nailed my taste. But Pandora relies on manual tagging by music experts. Fantastic, but probably not very scalable.
Not available where I live.
Re: The lie of music discovery algorithms
#177Earlier quoted context omitted.
Oh, my bad! It's a "landing page"! Totally normal that it needs to 200 Kb of JS to display 1 Kb of text!
There’s an email subscription field thats why i need js hahah you’re so fond of yourself
Don't take it personally though, JS abuse is unfortunately common; just need to vent for once. But like the author of article I linked, people tend to just skip those blank pages. It's a tiny minority which is probably not the target audience anyway. Actual hackers would look at how you did it and make their own version.
Re: The lie of music discovery algorithms
#178the underlying assumption for these systems for most people seems to be that all songs are treated equally.
this is clearly no longer the case for any major streaming platform. their own logic to promote might be too egregious now. same seems like for shuffling through a large playlist.
one can try to empathize to the ones designing this (e.g. shuffle anticipating network drops and switch to cached results for the next track) but self-discovery will remain evergreen.
Re: The lie of music discovery algorithms
#179So I've been using Tidal for 5 years now, and feel they run circles around Spotify in terms of curation. Their algorithms and curated tracks are better, and they steer away from the social/gamification features and lean in to artist-centric features. For example, they've had a "credits" feature since day one - you can look up the producer, guitarist, oboeist, etc of any song, and see what other work they've done. In…
Importantly for music discovery, Tidal make it relatively easy to browse by record label so if you're not listening to major label pop stuff you can pretty easily find a bunch of artists in a similar niche. If that is possible on Spotify they've made it hard enough to access that it might as well not have been there.
Last.fm was good, but inevitably went downhill when it was bought out. As they had to commercialise more and more from the early days as a uni project that became audioscrobbler, with a tiny userbase, they followed the standard Doctorow model of capitalist decline. They had a sweet spot a few years in when they had plenty of data flowing and they hadn't yet messed up their APIs. Right now I use it to log my listening but I'm waiting for the email that says I'll need to move my data elsewhere.
Re: The lie of music discovery algorithms
#180Earlier quoted context omitted.
I'm not sure how the Spotify recommendation algorithm works at all, but for some reason I imagined them doing fancier things than looking at my liked songs and finding similar ones. I would've thought they'd build a profile of you, and then find similar user profiles and show you songs those folks liked that you hadn't found yet. That's gotta be how they do it, right? I'm probably wrong.
I don't work at Spotify anymore and I didn't work on the tech I'm describing, but I picked up a bit about what was going on while there. First, there is/was no single algorithm, but the core ideas driving a lot of recommendations is: 1. Create user taste vectors 2. Match those vectors to other users or collections of tracks 3. Use that information and combinations of other things to find recommendations. Each step of…
"Other things" including intentional commercial biases presumably?
No matter what I do in Spotify, under several different rounds of accounts, it always seems to gravitate towards the tastes of the general public, i.e. some form of mass-market pop.
Their recent "ai" assistant was a slight improvement because you can ask it for less popular music which is typically better for music discovery.