Ai 191 flight status tracker5/26/2023 What impressed me was that it recommended quite a lot of numbers in the same key! It was funny clicking through them and the tonal centre being unchanged. It even recommended me more Carole King which is I guess shows it's picking up on something constant in her music. I love that it recommended other slow but rhythmic piano vamps with tender vocals by artists singing in other languages, some modern, some old, as well as some sung by men. One of the first tracks I tried was Natural Woman by Carole King. So often eg Spotify will insist I check out other bands in the same "scene" or from the same era as other artists I like, and in genres as broad as "70s rock" it can be really tiring. This is actually great, one of the most promising recommendation algorithms I've come across. Was there a conflict between the old school ML of the Echo Nest people and the new fancy neural net kids? Or was it just, as GP alludes to, that the NN methods were just too computationally expensive and they failed to justify their costs to leadership? They were headhunted by DeepMind, and have had a VERY impressive track record there over the years.Īnd I wonder why. I asked him, and Dieleman was allowed to say that the thing they built was one of the inputs into the then-new Discover Weekly, which made headlines for how outrageously good it was.īut Dieleman and v.d.Oord did not stay at Spotify. We know a bit of what they worked on, because Dieleman blogged on it, and indeed it was something a lot like what OP has made here - only better, I would say. A few years ago, Spotify had two young interns, Sander Dieleman and Aäron van den Oord. I doubt that "why does your product suck" is one of the things a Spotify employee is allowed to talk freely about in public!īut I've been watching them, I will speculate. And yeah, Spotify employees will complain about this more than anyone else, all the time :) at play as well, because in streaming services it's always there, in very bizarre waysīasically every single tweak to recommendations will break them. there's probably stuff about licensing, availability, contracts etc. some users a heavily weigted to only a few artists, some users listen to evereything and anything. some users mostly prefer curated suggestions, some users want ranodm stuff. Some of these users are the same user, but on different days some users want more of the same, some users want a more diverse listening experience. But it can also be hampered by extremely conflicting requirements (where "some" both means double-digit procent of users and these "some"s overlap with each other): But there's definitely continuous work done on them. I honestly don't know much about recommendations (and what I know I probably cannot tell). I also think it would be interesting if there was a way to specify two different songs to find either only the common things and/or to find what the fusion of those two tracks produces.īecause, as I said above, it's a very complex problem :) I look forward to seeing where this goes. This feels very promising since it clearly is picking up the styling of the specific songs across different genres and languages. It feels like it's matching multiple samples from the song instead of the whole song. Other songs have the vocals but no epic backing. Like it got, in my subjective description, the epic violin in orchestral music, but it completely ignores the fusion between the distict styles of traditional indian singing/instrumentals and western ochestral and also ignores the call response structure between the violin and carnatic players, which is the what I actually care about. I put in this track expecting other fusion songs to pop up, and arguably some do, but much more often it feels like a 20 second section was used to define the original song and it misses the underlying concept. Take Raga's Dance by Vanessa-Mae, A R Rahman. I'm not sure what aspect you're using to order the results, but having extra metadata to filter or group the results in some way would help a lot. I think locking one or more of those dimensions would allow for much better recommendations. ![]() ![]() There's no tempo consistency or genre consistency or even main instrument/vocal timbre consistency between recommendations. It absolutely finds similar sounding tracks, but it doesn't distinguish which part of the song made it enjoyable. This is very interesting, but unfortunately I haven't had the greatest luck in finding new songs I would enjoy listening to.
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